Models that contain the Current : I T low threshold

("Transient"; rapidly inactivating, threshold negative to -65mV)
Re-display model names without descriptions
    Models   Description
1.  A Model Circuit of Thalamocortical Convergence (Behuret et al. 2013)
“… Using dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. … The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. …”
2.  A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)
This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
3.  A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)
A realistic neural network was used to simulate a region of neocortex to obtain extracellular LFPs from ‘virtual micro-electrodes’ and produce test data for comparison with multisite microelectrode recordings. A model was implemented in the GENESIS neurosimulator. A simulated region of cortex was represented by layers 2/3, 5/6 (interneurons and pyramidal cells) and layer 4 stelate cells, spaced at 25 µm in each horizontal direction. Pyramidal cells received AMPA and NMDA inputs from neighboring cells at the basal and apical dendrites. The LFP data was generated by simulating 16-site electrode array with the help of ‘efield’ objects arranged at the predetermined positions with respect to the surface of the simulated network. The LFP for the model is derived from a weighted average of the current sources summed over all cellular compartments. Cell models were taken from from Traub et al. (2005) J Neurophysiol 93(4):2194-232.
4.  A single column thalamocortical network model (Traub et al 2005)
To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary.
5.  A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)
This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.
6.  A unified thalamic model of multiple distinct oscillations (Li, Henriquez and Fröhlich 2017)
We present a unified model of the thalamus that is capable of independently generating multiple distinct oscillations (delta, spindle, alpha and gamma oscillations) under different levels of acetylcholine (ACh) and norepinephrine (NE) modulation corresponding to different physiological conditions (deep sleep, light sleep, relaxed wakefulness and attention). The model also shows that entrainment of thalamic oscillations is state-dependent.
7.  Action potential of mouse urinary bladder smooth muscle (Mahapatra et al 2018)
Urinary incontinence is associated with enhanced spontaneous phasic contractions of the detrusor smooth muscle (DSM). Although a complete understanding of the etiology of these spontaneous contractions is not yet established, it is suggested that the spontaneously evoked action potentials (sAPs) in DSM cells initiate and modulate the contractions. In order to further our understanding of the ionic mechanisms underlying sAP generation, we present here a biophysically detailed computational model of a single DSM cell. First, we constructed mathematical models for nine ion channels found in DSM cells based on published experimental data: two voltage-gated Ca2+ ion channels, an hyperpolarization-activated ion channel, two voltage-gated K+ ion channels, three Ca2+-activated K+ ion channels and a non-specific background leak ion channel. Incorporating these channels, our DSM model is capable of reproducing experimentally recorded spike-type sAPs of varying configurations, ranging from sAPs displaying after-hyperpolarizations to sAPs displaying after-depolarizations. Our model, constrained heavily by physiological data, provides a powerful tool to investigate the ionic mechanisms underlying the genesis of DSM electrical activity, which can further shed light on certain aspects of urinary bladder function and dysfunction.
8.  Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007)
"In this study, we developed a general description of parameter combinations for which specified characteristics of neuronal or network activity are constant. Our approach is based on the implicit function theorem and is applicable to activity characteristics that smoothly depend on parameters. Such smoothness is often intrinsic to neuronal systems when they are in stable functional states. The conclusions about how parameters compensate each other, developed in this study, can thus be used even without regard to the specific mathematical model describing a particular neuron or neuronal network. ..."
9.  Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)
These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.
10.  Activity dependent conductances in a neuron model (Liu et al. 1998)
"... We present a model of a stomatogastric ganglion (STG) neuron in which several Ca2+-dependent pathways are used to regulate the maximal conductances of membrane currents in an activity-dependent manner. Unlike previous models of this type, the regulation and modification of maximal conductances by electrical activity is unconstrained. The model has seven voltage-dependent membrane currents and uses three Ca2+ sensors acting on different time scales. ... The model suggests that neurons may regulate their conductances to maintain fixed patterns of electrical activity, rather than fixed maximal conductances, and that the regulation process requires feedback systems capable of reacting to changes of electrical activity on a number of different time scales."
11.  Activity patterns in a subthalamopallidal network of the basal ganglia model (Terman et al 2002)
"Based on recent experimental data, we have developed a conductance-based computational network model of the subthalamic nucleus and the external segment of the globus pallidus in the indirect pathway of the basal ganglia. Computer simulations and analysis of this model illuminate the roles of the coupling architecture of the network, and associated synaptic conductances, in modulating the activity patterns displayed by this network. Depending on the relationships of these coupling parameters, the network can support three general classes of sustained firing patterns: clustering, propagating waves, and repetitive spiking that may show little regularity or correlation. ...". Terman's XPP code and a partial implementation by Taylor Malone in NEURON and python are included.
12.  Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005)
"We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. ... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction."
13.  Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)
" ... we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application. ..."
14.  Allen Institute: Gad2-IRES-Cre VISp layer 5 472447460
This is an Allen Cell Types Database model of a Gad2-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
15.  Allen Institute: Gad2-IRES-Cre VISp layer 5 473561729
This is an Allen Cell Types Database model of a Gad2-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
16.  Allen Institute: Htr3a-Cre VISp layer 2/3 472352327
This is an Allen Cell Types Database model of a Htr3a-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
17.  Allen Institute: Htr3a-Cre VISp layer 2/3 472421285
This is an Allen Cell Types Database model of a Htr3a-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
18.  Allen Institute: Nr5a1-Cre VISp layer 2/3 473862496
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
19.  Allen Institute: Nr5a1-Cre VISp layer 4 329322394
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
20.  Allen Institute: Nr5a1-Cre VISp layer 4 472306544
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
21.  Allen Institute: Nr5a1-Cre VISp layer 4 472442377
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
22.  Allen Institute: Nr5a1-Cre VISp layer 4 472451419
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
23.  Allen Institute: Nr5a1-Cre VISp layer 4 472915634
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
24.  Allen Institute: Nr5a1-Cre VISp layer 4 473834758
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
25.  Allen Institute: Nr5a1-Cre VISp layer 4 473863035
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
26.  Allen Institute: Nr5a1-Cre VISp layer 4 473871429
This is an Allen Cell Types Database model of a Nr5a1-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
27.  Allen Institute: Ntsr1-Cre VISp layer 4 472430904
This is an Allen Cell Types Database model of a Ntsr1-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
28.  Allen Institute: Pvalb-IRES-Cre VISp layer 2/3 472306616
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
29.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 471085845
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
30.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 472349114
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
31.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 472912177
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
32.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 473465774
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
33.  Allen Institute: Pvalb-IRES-Cre VISp layer 5 473862421
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
34.  Allen Institute: Pvalb-IRES-Cre VISp layer 6a 471081668
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
35.  Allen Institute: Pvalb-IRES-Cre VISp layer 6a 472301074
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
36.  Allen Institute: Pvalb-IRES-Cre VISp layer 6a 473860269
This is an Allen Cell Types Database model of a Pvalb-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
37.  Allen Institute: Rbp4-Cre VISp layer 5 472424854
This is an Allen Cell Types Database model of a Rbp4-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
38.  Allen Institute: Rbp4-Cre VISp layer 6a 473871592
This is an Allen Cell Types Database model of a Rbp4-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
39.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 2/3 472299294
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
40.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 2/3 472434498
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
41.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 4 473863510
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
42.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 5 471087975
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
43.  Allen Institute: Rorb-IRES2-Cre-D VISp layer 5 473561660
This is an Allen Cell Types Database model of a Rorb-IRES2-Cre-D neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
44.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472300877
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
45.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472427533
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
46.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 472912107
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
47.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 4 473465456
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
48.  Allen Institute: Scnn1a-Tg2-Cre VISp layer 5 472306460
This is an Allen Cell Types Database model of a Scnn1a-Tg2-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
49.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 329321704
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
50.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 472363762
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
51.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 473862845
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
52.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 4 473872986
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
53.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 472455509
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
54.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 473863578
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
55.  Allen Institute: Scnn1a-Tg3-Cre VISp layer 5 473871773
This is an Allen Cell Types Database model of a Scnn1a-Tg3-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
56.  Allen Institute: Sst-IRES-Cre VISp layer 2/3 471086533
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
57.  Allen Institute: Sst-IRES-Cre VISp layer 2/3 472304676
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 2/3 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
58.  Allen Institute: Sst-IRES-Cre VISp layer 4 472304539
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 4 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
59.  Allen Institute: Sst-IRES-Cre VISp layer 5 472299363
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
60.  Allen Institute: Sst-IRES-Cre VISp layer 5 472450023
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
61.  Allen Institute: Sst-IRES-Cre VISp layer 5 473835796
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 5 of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
62.  Allen Institute: Sst-IRES-Cre VISp layer 6a 472440759
This is an Allen Cell Types Database model of a Sst-IRES-Cre neuron from layer 6a of the mouse primary visual cortex. The model was based on a traced morphology after filling the cell with biocytin and optimized using experimental electrophysiology data recorded from the same cell. The electrophysiology data was collected in a highly standardized way to facilitate comparison across all cells in the database. The model was optimized by a genetic algorithm that adjusted the densities of conductances placed at the soma to match experimentally-measured features of action potential firing. Data and models from the Allen Cell Types Database are made available to the community under the Allen Institute's Terms of Use and Citation Policy.
63.  Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010)
The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.
64.  AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008)
This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike, and how the somatic spike threshold variance is affected by spike propagation.
65.  Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003)
"... we measured T-type current of isolated goldfish retinal ganglion cells with perforated-patch voltageclamp methods in solutions containing a normal extracellular Ca2+ concentration. The voltage sensitivities and rates of current activation, inactivation, deactivation, and recovery from inactivation were similar to those of expressed +1G (CaV3.1) Ca2+ channel clones, except that the rate of deactivation was significantly faster. We reproduced the amplitude and kinetics of measured T currents with a numerical simulation based on a kinetic model developed for an +1G Ca2+ channel. Finally, we show that this model predicts the increase of T-type current made available between resting potential and spike threshold by repetitive hyperpolarizations presented at rates that are within the bandwidth of signals processed in situ by these neurons."
66.  Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008)
Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells. Traub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience.
67.  Axonal NaV1.6 Sodium Channels in AP Initiation of CA1 Pyramidal Neurons (Royeck et al. 2008)
"... We show that the Na+ channel NaV1.6 displays a striking aggregation at the AIS of cortical neurons. ... In combination with simulations using a realistic computer model of a CA1 pyramidal cell, our results imply that a hyperpolarized voltage-dependence of activation of AIS NaV1.6 channels is important both in determining spike threshold and localizing spike initiation to the AIS. ... These results suggest that NaV1.6 subunits at the AIS contribute significantly to its role as spike trigger zone and shape repetitive discharge properties of CA1 neurons."
68.  Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2011)
This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency.
69.  Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)
"Variations in cortical oscillations in the alpha (7–14 Hz) and beta (15–29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ..."
70.  Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)
"Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. ..."
71.  CA1 pyramidal neuron (Combe et al 2018)
"Gamma oscillations are thought to play a role in learning and memory. Two distinct bands, slow (25-50 Hz) and fast (65-100 Hz) gamma, have been identified in area CA1 of the rodent hippocampus. Slow gamma is phase-locked to activity in area CA3 and presumably driven by the Schaffer collaterals. We used a combination of computational modeling and in vitro electrophysiology in hippocampal slices of male rats to test whether CA1 neurons responded to Schaffer collateral stimulation selectively at slow gamma frequencies, and to identify the mechanisms involved. Both approaches demonstrated that in response to temporally precise input at Schaffer collaterals, CA1 pyramidal neurons fire preferentially in the slow gamma range regardless of whether the input is at fast or slow gamma frequencies, suggesting frequency selectivity in CA1 output with respect to CA3 input. In addition, phase-locking, assessed by the vector strength, was more precise for slow gamma than fast gamma input. ..."
72.  CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)
We developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings, and combining results for two different response measures (peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains), and two different spatial integration conditions (within vs. between-branch summation), we found the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.
73.  CA1 pyramidal neuron: functional significance of axonal Kv7 channels (Shah et al. 2008)
The model used in this paper confirmed the experimental findings suggesting that axonal Kv7 channels are critically and uniquely required for determining the inherent spontaneous firing of hippocampal CA1 pyramids, independently of alterations in synaptic activity. The model predicts that the axonal Kv7 density could be 3-5 times that at the soma.
74.  CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)
This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer collateral synapse stimulation. In the model, the peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value.
75.  CA1 pyramidal neurons: effects of a Kv7.2 mutation (Miceli et al. 2009)
NEURON mod files from the paper: Miceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor of K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009). In this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2 genes encoding for the neuronal KM current in a case of benign familial neonatal seizures, increased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition.
76.  CA3 pyramidal neuron (Lazarewicz et al 2002)
The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst.
77.  CA3 Pyramidal Neuron (Migliore et al 1995)
Model files from the paper: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). Demonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.
78.  CA3 pyramidal neuron (Safiulina et al. 2010)
In this review some of the recent work carried out in our laboratory concerning the functional role of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has been highlighted. To compare the relative strength of CA3 pyramidal cell output in relation to their MF glutamatergic or GABAergic inputs in postnatal development, a realistic model was constructed taking into account the different biophysical properties of these synapses.
79.  CA3 pyramidal neuron: firing properties (Hemond et al. 2008)
In the paper, this model was used to identify how relative differences in K+ conductances, specifically KC, KM, & KD, between cells contribute to the different characteristics of the three types of firing patterns observed experimentally.
80.  Calcium and potassium currents of olfactory bulb juxtaglomerular cells (Masurkar and Chen 2011)
Inward and outward currents of the olfactory bulb juxtaglomerular cells are characterized in the experiments and modeling in these two Masurkar and Chen 2011 papers.
81.  Calcium spikes in basal dendrites (Kampa and Stuart 2006)
This model was published in Kampa & Stuart (2006) J Neurosci 26(28):7424-32. The simulation creates two plots showing voltage and calcium changes in basal dendrites of layer 5 pyramidal neurons during action potential backpropagation. created by B. Kampa (2006)
82.  Calcium waves and mGluR-dependent synaptic plasticity in CA1 pyr. neurons (Ashhad & Narayanan 2013)
A morphologically realistic, conductance-based model equipped with kinetic schemes that govern several calcium signalling modules and pathways in CA1 pyramidal neurons
83.  Cerebellar Golgi cell (Solinas et al. 2007a, 2007b)
"... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting."
84.  Cerebellar nuclear neuron (Sudhakar et al., 2015)
"... In this modeling study, we investigate different forms of Purkinje neuron simple spike pause synchrony and its influence on candidate coding strategies in the cerebellar nuclei. That is, we investigate how different alignments of synchronous pauses in synthetic Purkinje neuron spike trains affect either time-locking or rate-changes in the downstream nuclei. We find that Purkinje neuron synchrony is mainly represented by changes in the firing rate of cerebellar nuclei neurons. ..."
85.  Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)
This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
86.  Cerebellar purkinje cell (De Schutter and Bower 1994)
Tutorial simulation of a cerebellar Purkinje cell. This tutorial is based upon a GENESIS simulation of a cerebellar Purkinje cell, modeled and fine-tuned by Erik de Schutter. The tutorial assumes that you have a basic knowledge of the Purkinje cell and its synaptic inputs. It gives visual insight in how different properties as concentrations and channel conductances vary and interact within a real Purkinje cell.
87.  Cerebellar purkinje cell: K and Ca channels regulate APs (Miyasho et al 2001)
We adopted De Schutter and Bower's model as the starting point, then modified the descriptions of several ion channels, such as the P-type Ca channel and the delayed rectifier K channel, and added class-E Ca channels and D-type K channels to the model. Our new model reproduces most of our experimental results and supports the conclusions of our experimental study that class-E Ca channels and D-type K channels are present and functioning in the dendrites of Purkinje neurons.
88.  Channel density variability among CA1 neurons (Migliore et al. 2018)
The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons.
89.  Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
"A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. ... One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. ... The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al. 2005), with activity evoked by different thalamic stimuli."
90.  Complex CA1-neuron to study AP initiation (Wimmer et al. 2010)
Complex model of a pyramidal CA1-neuron, adapted from Royeck, M., et al. Role of axonal NaV1.6 sodium channels in action potential initiation of CA1 pyramidal neurons. Journal of neurophysiology 100, 2361-2380 (2008). It contains a biophysically realistic morphology comprising 265 compartments (829 segments) and 15 different distributed Ca2+- and/or voltage-dependent conductances.
91.  Computational model of bladder small DRG neuron soma (Mandge & Manchanda 2018)
Bladder small DRG neurons, which are putative nociceptors pivotal to urinary bladder function, express more than a dozen different ionic membrane mechanisms: ion channels, pumps and exchangers. Small-conductance Ca2+-activated K+ (SKCa) channels which were earlier thought to be gated solely by intracellular Ca2+ concentration ([Ca]i ) have recently been shown to exhibit inward rectification with respect to membrane potential. The effect of SKCa inward rectification on the excitability of these neurons is unknown. Furthermore, studies on the role of KCa channels in repetitive firing and their contributions to different types of afterhyperpolarization (AHP) in these neurons are lacking. In order to study these phenomena, we first constructed and validated a biophysically detailed single compartment model of bladder small DRG soma constrained by physiological data. The model includes twenty-two major known membrane mechanisms along with intracellular Ca2+ dynamics comprising Ca2+ diffusion, cytoplasmic buffering, and endoplasmic reticulum (ER) and mitochondrial mechanisms. Using modelling studies, we show that inward rectification of SKCa is an important parameter regulating neuronal repetitive firing and that its absence reduces action potential (AP) firing frequency. We also show that SKCa is more potent in reducing AP spiking than the large-conductance KCa channel (BKCa) in these neurons. Moreover, BKCa was found to contribute to the fast AHP (fAHP) and SKCa to the medium-duration (mAHP) and slow AHP (sAHP). We also report that the slow inactivating A-type K+ channel (slow KA) current in these neurons is composed of 2 components: an initial fast inactivating (time constant ~ 25-100 ms) and a slow inactivating (time constant ~ 200-800 ms) current. We discuss the implications of our findings, and how our detailed model can help further our understanding of the role of C-fibre afferents in the physiology of urinary bladder as well as in certain disorders.
92.  Computer model of clonazepam`s effect in thalamic slice (Lytton 1997)
Demonstration of the effect of a minor pharmacological synaptic change at the network level. Clonazepam, a benzodiazepine, enhances inhibition but is paradoxically useful for certain types of seizures. This simulation shows how inhibition of inhibitory cells (the RE cells) produces this counter-intuitive effect.
93.  Computer simulations of neuron-glia interactions mediated by ion flux (Somjen et al. 2008)
"... To examine the effect of glial K+ uptake, we used a model neuron equipped with Na+, K+, Ca2+ and Cl− conductances, ion pumps and ion exchangers, surrounded by interstitial space and glia. The glial membrane was either “passive”, incorporating only leak channels and an ion exchange pump, or it had rectifying K+ channels. We computed ion fluxes, concentration changes and osmotic volume changes. ... We conclude that voltage gated K+ currents can boost the effectiveness of the glial “potassium buffer” and that this buffer function is important even at moderate or low levels of excitation, but especially so in pathological states."
94.  Convergence regulates synchronization-dependent AP transfer in feedforward NNs (Sailamul et al 2017)
We study how synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. We implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
95.  Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)
" ... We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. ..."
96.  Current Dipole in Laminar Neocortex (Lee et al. 2013)
Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009. https://bitbucket.org/jonescompneurolab/corticaldipole
97.  Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
"... High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) suppresses parkinsonian motor symptoms and modulates cortical activity. ... Cortical evoked potentials (cEP) generated by STN DBS reflect the response of cortex to subcortical stimulation, and the goal was to determine the neural origin of cEP using a two-step approach. First, we recorded cEP over ipsilateral primary motor cortex during different frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned parkinsonian rats. Second, we used a biophysically-based model of the thalamocortical network to deconstruct the neural origin of the cEP. The in vivo cEP included short (R1), intermediate (R2) and long-latency (R3) responses. Model-based cortical responses to simulated STN DBS matched remarkably well the in vivo responses. R1 was generated by antidromic activation of layer 5 pyramidal neurons, while recurrent activation of layer 5 pyramidal neurons via excitatory axon collaterals reproduced R2. R3 was generated by polysynaptic activation of layer 2/3 pyramidal neurons via the cortico-thalamic-cortical pathway. Antidromic activation of the hyperdirect pathway and subsequent intracortical and cortico-thalamo-cortical synaptic interactions were sufficient to generate cEP by STN DBS, and orthodromic activation through basal ganglia-thalamus-cortex pathways was not required. These results demonstrate the utility of cEP to determine the neural elements activated by STN DBS that might modulate cortical activity and contribute to the suppression of parkinsonian symptoms."
98.  Dentate granule cell: mAHP & sAHP; SK & Kv7/M channels (Mateos-Aparicio et al., 2014)
The model is based on that of Aradi & Holmes (1999; Journal of Computational Neuroscience 6, 215-235). It was used to help understand the contribution of M and SK channels to the medium afterhyperpolarization (mAHP) following one or seven spikes, as well as the contribution of M channels to the slow afterhyperpolarization (sAHP). We found that SK channels are the main determinants of the mAHP, in contrast to CA1 pyramidal cells where the mAHP is primarily caused by the opening of M channels. The model reproduced these experimental results, but we were unable to reproduce the effects of the M-channel blocker XE991 on the sAHP. It is suggested that either the XE991-sensitive component of the sAHP is not due to M channels, or that when contributing to the sAHP, these channels operate in a mode different from that associated with the mAHP.
99.  Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999)
We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. Channel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. The model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. ... The model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. We conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges.
100.  Dentate gyrus network model (Santhakumar et al 2005)
Mossy cell loss and mossy fiber sprouting are two characteristic consequences of repeated seizures and head trauma. However, their precise contributions to the hyperexcitable state are not well understood. Because it is difficult, and frequently impossible, to independently examine using experimental techniques whether it is the loss of mossy cells or the sprouting of mossy fibers that leads to dentate hyperexcitability, we built a biophysically realistic and anatomically representative computational model of the dentate gyrus to examine this question. The 527-cell model, containing granule, mossy, basket, and hilar cells with axonal projections to the perforant-path termination zone, showed that even weak mossy fiber sprouting (10-15% of the strong sprouting observed in the pilocarpine model of epilepsy) resulted in the spread of seizure-like activity to the adjacent model hippocampal laminae after focal stimulation of the perforant path. See reference for more and details.
101.  Dentate gyrus network model (Tejada et al 2014)
" ... Here we adapted an existing computational model of the dentate gyrus (J Neurophysiol 93: 437-453, 2005) by replacing the reduced granule cell models with morphologically detailed models coming from (3D) reconstructions of mature cells. ... Different fractions of the mature granule cell models were replaced by morphologically reconstructed models of newborn dentate granule cells from animals with PILO-induced Status Epilepticus, which have apical dendritic alterations and spine loss, and control animals, which do not have these alterations. This complex arrangement of cells and processes allowed us to study the combined effect of mossy fiber sprouting, altered apical dendritic tree and dendritic spine loss in newborn granule cells on the excitability of the dentate gyrus model. Our simulations suggest that alterations in the apical dendritic tree and dendritic spine loss in newborn granule cells have opposing effects on the excitability of the dentate gyrus after Status Epilepticus. Apical dendritic alterations potentiate the increase of excitability provoked by mossy fiber sprouting while spine loss curtails this increase. "
102.  Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)
To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component.
103.  Differences between type A and B photoreceptors (Blackwell 2006)
In Hermissenda crassicornis, the memory of light associated with turbulence is stored as changes in intrinsic and synaptic currents in both type A and type B photoreceptors. These photoreceptor types exhibit qualitatively different responses to light and current injection, and these differences shape the spatiotemporal firing patterns that control behavior. Thus the objective of the study was to identify the mechanisms underlying these differences. The approach was to develop a type B model that reproduced characteristics of type B photoreceptors recorded in vitro, and then to create a type A model by modifying a select number of ionic currents. Comparison of type A models with characteristics of type A photoreceptors recorded in vitro revealed that type A and type B photoreceptors have five main differences, three that have been characterized experimentally and two that constitute hypotheses to be tested with experiments in the future. See paper for more and details.
104.  Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)
"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ..." See paper for more and details.
105.  Dopamine neuron of the vent. periaqu. gray and dors. raphe nucleus (vlPAG/DRN) (Dougalis et al 2017)
The following computer model describes the electrophysiological properties of dopamine (DA) neurons of the ventrolateral periaquaductal gray and dorsal raphe nucleus (vlPAG/DRN). the model and how to replicate Figures 7-10 of the manuscript (Dougalis et al., 2017 J Comput Neurosci). SUMMARY: We have conducted a voltage-clamp study to provide a kinetic description of major sodium, potassium and calcium ionic currents operant on adult DA vlPAG/DRN neurons in brain slices obtained from pitx3-GFP mice. Based on experimentally derived voltage-clamp data, we then constructed a simplified, conductance-based, Hodgkin and Huxley-type, computer model and validated its behaviour against in vitro neurophysiological data. Using simulations in the computational DA model, we explored the contribution of individual ionic currents in vlPAG/DRN DA neuron’s spontaneous firing, pacemaker frequency and threshold for spike frequency adaptation in silico. The data presented here extend our previous physiological characterization (Dougalis et al. 2012) and argue that DA neurons of the vlPAG/DRN express autorhythmicity in the absence of synaptic transmission via the interplay of potassium and sodium currents without the absolute need of calcium currents. The properties of the ionic currents recorded here (IH current, IA current), the lack of small oscillating potentials in the presence of sodium channel blockers taken together with the mechanisms for autorhythmicity (reliance more on sodium rather than calcium currents) also support further the idea that vlPAG/DRN DA neurons are operationally similar to VTA, rather than SNc, DA neurons. In particular, the properties of a slowly inactivating IA current in conjunction with the small and slowly activating IH current described herein pinpoint that vlPAG/DRN DA neurons are most similar to prefrontal cortex or medial shell of nucleus accumbens projecting DA neurons (see Lammel et al. 2008, 2011).
106.  Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)
"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..."
107.  Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)
"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..."
108.  Excitability of PFC Basal Dendrites (Acker and Antic 2009)
".. We carried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal branches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We found that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated sodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier potassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential propagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling (NEURON) was used to link the observed behavior with the underlying biophysical properties. The best-fit model included a non-uniform sodium channel distribution with decreasing conductance with distance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP amplitudes decline with distance in this model, but to a lesser extent than previously thought. We used this model to explore the mechanisms underlying two sets of published data involving high frequency trains of action potentials, and the local generation of sodium spikelets. ..."
109.  Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)
"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). ... This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. …" Implemented by Andrey Dovzhenok, to whom questions should be addressed.
110.  Frog second-order vestibular neuron models (Rossert et al. 2011)
This implements spiking Hodgkin-Huxley type models of tonic and phasic second-order vestibular neurons. Models fitted to intracellular spike and membrane potential recordings from frog (Rana temporaria). The models can be stimulated by intracellular step current, frequency current (ZAP) or synaptic stimulation.
111.  Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001)
"The electrical characteristics of many neurons are remarkably robust in the face of changing internal and external conditions. At the same time, neurons can be highly sensitive to neuromodulators. We find correlates of this dual robustness and sensitivity in a global analysis of the structure of a conductance-based model neuron. ..."
112.  High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)
" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic basal ganglia output with tonic, high frequency firing. In our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals. High frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result. We provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in normal, parkinsonian, and DBS states in a reduced model. This analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ..."
113.  Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)
"We review here the development of Hodgkin- Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are 'fast spiking', 'regular spiking', 'intrinsically bursting' and 'low-threshold spike' cells. For each class, we fit 'minimal' HH type models to experimental data. ..."
114.  Hypocretin and Locus Coeruleus model neurons (Carter et al 2012)
Conductance based model of the hypocretin neurons (HCRT) and another one of the Locus Coeruleus one (LC). The HCRT drive the LCs via the HCRT receptor on the LCs. The LCs lead to the awakening of the mice if the number of spikes raises over 10 spikes in 10 seconds window.
115.  Hysteresis in voltage gating of HCN channels (Elinder et al 2006, Mannikko et al 2005)
We found that HCN2 and HCN4 channels expressed in oocytes from the frog Xenopus laevis do not display the activation kinetic changes that we (previously) observed in spHCN and HCN1. However, HCN2 and HCN4 channels display changes in their tail currents, suggesting that these channels also undergo mode shifts and that the conformational changes underlying the mode shifts are due to conserved aspects of HCN channels. With computer modelling, we show that in channels with relatively slow opening kinetics and fast mode-shift transitions, such as HCN2 and HCN4 channels, the mode shift effects are not readily observable, except in the tail kinetics. Computer simulations of sino-atrial node action potentials suggest that the HCN2 channel, together with the HCN1 channel, are important regulators of the heart firing frequency and that the mode shift is an important property to prevent arrhythmic firing. We conclude that although all HCN channels appear to undergo mode shifts – and thus may serve to prevent arrhythmic firing – it is mainly observable in ionic currents from HCN channels with faster kinetics. See papers for more and details.
116.  IA and IT interact to set first spike latency (Molineux et al 2005)
Using patch clamp and modeling, we illustrate that spike latency characteristics are the product of an interplay between I(A) and low-threshold calcium current (I(T)) that requires a steady-state difference in the inactivation parameters of the currents. Furthermore, we show that the unique first-spike latency characteristics of stellate cells have important implications for the integration of coincident IPSPs and EPSPs, such that inhibition can shift first-spike latency to differentially modulate the probability of firing.
117.  Impact of dendritic atrophy on intrinsic and synaptic excitability (Narayanan & Chattarji, 2010)
These simulations examined the atrophy induced changes in electrophysiological properties of CA3 pyramidal neurons. We found these neurons change from bursting to regular spiking as atrophy increases. Region-specific atrophy induced region-specific increases in synaptic excitability in a passive dendritic tree. All dendritic compartments of an atrophied neuron had greater synaptic excitability and a larger voltage transfer to the soma than the control neuron.
118.  Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)
The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity. Our model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof.
119.  Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson’s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211–235, 2004), was developed. ... Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively. Our results are consistent with the experimental and the clinical evidences on the network effects of DBS."
120.  Ionic current model of a Hypoglossal Motoneuron (Purvis & Butera 2005)
"We have developed a single-compartment, electrophysiological, hypoglossal motoneuron (HM) model based primarily on experimental data from neonatal rat HMs. The model is able to reproduce the fine features of the HM action potential: the fast afterhyperpolarization, the afterdepolarization, and the medium-duration afterhyperpolarization (mAHP). The model also reproduces the repetitive firing properties seen in neonatal HMs and replicates the neuron’s response to pharmacological experiments. The model was used to study the role of specific ionic currents in HM firing and how variations in the densities of these currents may account for age dependent changes in excitability seen in HMs. ..."
121.  Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)
"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation. The single compartment model incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes (CaV3.1, CaV3.2, and CaV3.3). Our simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal pyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology. We also performed fastslow analysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction of two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation gate. The model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound. Finally, we use the model to simulate the effects of two epilepsy-linked mutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability."
122.  Knox implementation of Destexhe 1998 spike and wave oscillation model (Knox et al 2018)
" ...The aim of this study was to use an established thalamocortical computer model to determine how T-type calcium channels work in concert with cortical excitability to contribute to pathogenesis and treatment response in CAE. METHODS: The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA , and GABAB synapses. Network behavior was simulated for different combinations of T-type calcium channel conductance, inactivation time, steady state activation/inactivation shift, and cortical GABAA conductance. RESULTS: Decreasing cortical GABAA conductance and increasing T-type calcium channel conductance converted spindle to spike and wave oscillations; smaller changes were required if both were changed in concert. In contrast, left shift of steady state voltage activation/inactivation did not lead to spike and wave oscillations, whereas right shift reduced network propensity for oscillations of any type...."
123.  KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)
The output of the cerebellum to the motor axis of the central nervous system is orchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear (DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with KV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being constitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and regulates the temporal precision of self-sustained firing of these neurons. ... Through the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing and communication of information from the lateral DCN to thalamic relay nuclei is established.
124.  L5 PFC pyramidal neurons (Papoutsi et al. 2017)
" ... Here, we use a modeling approach to investigate whether and how the morphology of the basal tree mediates the functional output of neurons. We implemented 57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of the rat and identified morphological types which were characterized by different response features, forming distinct functional types. These types were robust to a wide range of manipulations (distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology or the number of activated synapses) and supported different temporal coding schemes at both the single neuron and the microcircuit level. We predict that the basal tree morphological diversity among neurons of the same class mediates their segregation into distinct functional pathways. ..."
125.  L5b PC model constrained for BAC firing and perisomatic current step firing (Hay et al., 2011)
"... L5b pyramidal cells have been the subject of extensive experimental and modeling studies, yet conductance-based models of these cells that faithfully reproduce both their perisomatic Na+-spiking behavior as well as key dendritic active properties, including Ca2+ spikes and back-propagating action potentials, are still lacking. Based on a large body of experimental recordings from both the soma and dendrites of L5b pyramidal cells in adult rats, we characterized key features of the somatic and dendritic firing and quantified their statistics. We used these features to constrain the density of a set of ion channels over the soma and dendritic surface via multi-objective optimization with an evolutionary algorithm, thus generating a set of detailed conductance-based models that faithfully replicate the back-propagating action potential activated Ca2+ spike firing and the perisomatic firing response to current steps, as well as the experimental variability of the properties. ... The models we present provide several experimentally-testable predictions and can serve as a powerful tool for theoretical investigations of the contribution of single-cell dynamics to network activity and its computational capabilities. "
126.  Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)
" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. ... We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ..."
127.  LGMD with 3D morphology and active dendrites (Dewell & Gabbiani 2018)
This is a model of the locust LGMD looming sensitive neuron from Dewell & Gabbiani 2018. The morphology was constructed based on 2-photon imaging, and active conductances throughout the neuron were based on sharp electrode recordings in vivo.
128.  Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. ... The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition..."
129.  Low Threshold Calcium Currents in TC cells (Destexhe et al 1998)
In Destexhe, Neubig, Ulrich, and Huguenard (1998) experiments and models examine low threshold calcium current's (IT, or T-current) distribution in thalamocortical (TC) cells. Multicompartmental modeling supports the hypothesis that IT currents have a density at least several fold higher in the dendrites than the soma. The IT current contributes significantly to rebound bursts and is thought to have important network behavior consequences. See the paper for details. See also http://cns.iaf.cnrs-gif.fr Correspondance may be addressed to Alain Destexhe: Destexhe@iaf.cnrs-gif.fr
130.  Low Threshold Calcium Currents in TC cells (Destexhe et al 1998) (Brian)
R Brette's implementation in Brian 2 of Destexhe et al 1998's model. The author's original code is also available from ModelDB with accession number 279 (yes, was one of the first models in ModelDB)!
131.  Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron (Traub et al 2003)
This simulation is based on the reference paper listed below. This port was made by Roger D Traub and Maciej T Lazarewicz (mlazarew at seas.upenn.edu) Thanks to Ashlen P Reid for help with porting a morphology of the cell.
132.  Medial vestibular neuron models (Quadroni and Knopfel 1994)
The structure and the parameters of the model cells were chosen to reproduce the responses of type A and type B MVNns as described in electrophysiological recordings. The emergence of oscillatory firing under these two specific experimental conditions is consistent with electrophysiological recordings not used during construction of the model. We, therefore, suggest that these models have a high predictive value.
133.  MEG of Somatosensory Neocortex (Jones et al. 2007)
"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception."
134.  Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)
"... We simulated detailed conductance-based models of TTCs (Layer 5 thick tufted pyramidal cells) forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. ... Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex. "
135.  Midbrain torus semicircularis neuron model (Aumentado-Armstrong et al. 2015)
This paper investigates how midbrain electrosensory neurons give invariant responses to natural communication stimuli. A model explains that such invariance can be achieved by combining afferent input from ON and OFF cells.
136.  Model of the cerebellar granular network (Sudhakar et al 2017)
"The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. ..."
137.  Multiscale simulation of the striatal medium spiny neuron (Mattioni & Le Novere 2013)
"… We present a new event-driven algorithm to synchronize different neuronal models, which decreases computational time and avoids superfluous synchronizations. The algorithm is implemented in the TimeScales framework. We demonstrate its use by simulating a new multiscale model of the Medium Spiny Neuron of the Neostriatum. The model comprises over a thousand dendritic spines, where the electrical model interacts with the respective instances of a biochemical model. Our results show that a multiscale model is able to exhibit changes of synaptic plasticity as a result of the interaction between electrical and biochemical signaling. …"
138.  Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)
" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..."
139.  MyFirstNEURON (Houweling, Sejnowski 1997)
MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation.
140.  Neuronal dendrite calcium wave model (Neymotin et al, 2015)
"... We developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate (IP3 ), diffusible Ca2+, IP3 receptors (IP3 Rs), endoplasmic reticulum (ER) Ca2+ leak, and ER pump (SERCA) on ER. ... At least two modes of Ca2+ wave spread have been suggested: a continuous mode based on presumed relative homogeneity of ER within the cell; and a pseudo-saltatory model where Ca2+ regeneration occurs at discrete points with diffusion between them. We compared the effects of three patterns of hypothesized IP3 R distribution: 1. continuous homogeneous ER, 2. hotspots with increased IP3R density (IP3 R hotspots), 3. areas of increased ER density (ER stacks). All three modes produced Ca2+ waves with velocities similar to those measured in vitro (~50 - 90µm /sec). ... The measures were sensitive to changes in density and spacing of IP3 R hotspots and stacks. ... An extended electrochemical model, including voltage gated calcium channels and AMPA synapses, demonstrated that membrane priming via AMPA stimulation enhances subsequent Ca2+ wave amplitude and duration. Our modeling suggests that pharmacological targeting of IP3 Rs and SERCA could allow modulation of Ca2+ wave propagation in diseases where Ca2+ dysregulation has been implicated. "
141.  NMDA subunit effects on Calcium and STDP (Evans et al. 2012)
Effect of NMDA subunit on spike timing dependent plasticity.
142.  Nodose sensory neuron (Schild et al. 1994, Schild and Kunze 1997)
This is a simulink implementation of the model described in Schild et al. 1994, and Schild and Kunze 1997 papers on Nodose sensory neurons. These papers describe the sensitivity these models have to their parameters and the match of the models to experimental data.
143.  O-LM interneuron model (Lawrence et al. 2006)
Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties.
144.  Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)
Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms.
145.  Paradoxical effect of fAHP amplitude on gain in dentate gyrus granule cells (Jaffe & Brenner 2018)
The afterhyperpolarization (AHP) is canonically viewed as a major factor underlying the refractory period, serving to limit neuronal firing rate. We recently reported (Wang et al, J. Neurophys. 116:456, 2016) that enhancing the amplitude of the fast AHP in a relatively slowly firing neuron (versus fast spiking neurons), augments neuronal excitability in dentate gyrus granule neurons expressing gain-of-function BK channels. Here we present a novel, quantitative hypothesis for how varying the amplitude of the fast AHP (fAHP) can, paradoxically, influence a subsequent spike tens of milliseconds later.
146.  Paradoxical GABA-mediated excitation (Lewin et al. 2012)
"GABA is the key inhibitory neurotransmitter in the adult central nervous system, but in some circumstances can lead to a paradoxical excitation that has been causally implicated in diverse pathologies from endocrine stress responses to diseases of excitability including neuropathic pain and temporal lobe epilepsy. We undertook a computational modeling approach to determine plausible ionic mechanisms of GABAA-dependent excitation in isolated post-synaptic CA1 hippocampal neurons because it may constitute a trigger for pathological synchronous epileptiform discharge. In particular, the interplay intracellular chloride accumulation via the GABAA receptor and extracellular potassium accumulation via the K/Cl co-transporter KCC2 in promoting GABAA-mediated excitation is complex. ..."
147.  Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)
Simulation and fitting of two-compartment (active soma, passive dendrite) for different classes of cortical neurons. The fitting technique indirectly matches neuronal currents derived from somatic membrane potential data rather than fitting the voltage traces directly. The method uses an analytic solution for the somatic ion channel maximal conductances given approximate models of the channel kinetics, membrane dynamics and dendrite. This approach is tested on model-derived data for various cortical neurons.
148.  Pleiotropic effects of SCZ-associated genes (Mäki-Marttunen et al. 2017)
Python and MATLAB scripts for studying the dual effects of SCZ-related genes on layer 5 pyramidal cell firing and sinoatrial node cell pacemaking properties. The study is based on two L5PC models (Hay et al. 2011, Almog & Korngreen 2014) and SANC models (Kharche et al. 2011, Severi et al. 2012).
149.  Preserving axosomatic spiking features despite diverse dendritic morphology (Hay et al., 2013)
The authors found that linearly scaling the ion channel conductance densities of a reference model with the conductance load in 28 3D reconstructed layer 5 thick-tufted pyramidal cells was necessary to match the experimental statistics of these cells electrical firing properties.
150.  Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)
"... As a practical demonstration of the use of CoCoDat we constructed a detailed computer model of an intrinsically bursting (IB) layer V pyramidal neuron from the rat barrel cortex supplementing experimental data (Schubert et al., 2001) with information extracted from the database. The pyramidal neuron morphology (Fig. 10B) was reconstructed from histological sections of a biocytin-stained IB neuron using the NeuroLucida software package..."
151.  Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)
This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity
152.  Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001)
This package shows single-compartment models of different classes of cortical neurons, such as the "regular-spiking", "fast-spiking" and "bursting" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html
153.  Rat LGN Thalamocortical Neuron (Connelly et al 2015, 2016)
" ... Here, combining data from fluorescence-targeted dendritic recordings and Ca2+ imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS (Low Threshold Spike) generation is established. ..." " ... Using dendritic recording, 2-photon glutamate uncaging, and computational modeling, we investigated how rat dorsal lateral geniculate nucleus thalamocortical neurons integrate excitatory corticothalamic feedback. ..."
154.  Rat phrenic motor neuron (Amini et al 2004)
We have developed a model for the rat phrenic motor neuron (PMN) that robustly replicates many experimentally observed behaviors of PMNs in response to pharmacological, ionic, and electrical perturbations using a single set of parameters.
155.  Rat subthalamic projection neuron (Gillies and Willshaw 2006)
A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details.
156.  Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)
A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.
157.  Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)
We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates.
158.  Robust transmission in the inhibitory Purkinje Cell to Cerebellar Nuclei pathway (Abbasi et al 2017)
159.  Role of the AIS in the control of spontaneous frequency of dopaminergic neurons (Meza et al 2017)
Computational modeling showed that the size of the Axon Initial Segment (AIS), but not its position within the somatodendritic domain, is the major causal determinant of the tonic firing rate in the intact model, by virtue of the higher intrinsic frequency of the isolated AIS. Further mechanistic analysis of the relationship between neuronal morphology and firing rate showed that dopaminergic neurons function as a coupled oscillator whose frequency of discharge results from a compromise between AIS and somatodendritic oscillators.
160.  Schiz.-linked gene effects on intrinsic single-neuron excitability (Maki-Marttunen et al. 2016)
Python scripts for running NEURON simulations that model a layer V pyramidal cell with certain genetic variants implemented. The genes included are obtained from genome-wide association studies of schizophrenia.
161.  SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)
" … Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia."
162.  Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)
" ... We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. ... The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures."
163.  Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)
This work (published in "Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001).
164.  Simulation study of Andersen-Tawil syndrome (Sung et al 2006)
Patients with Andersen-Tawil syndrome (ATS) mostly have mutations on the KCNJ2 gene producing loss of function or dominant-negative suppression of the inward rectifier K(+) channel Kir2.1. However, clinical manifestations of ATS including dysmorphic features, periodic paralysis (hypo-, hyper-, or normokalemic), long QT, and ventricular arrhythmias (VA) are considerably variable. Using a modified dynamic Luo-Rudy simulation model of cardiac ventricular myocyte, we elucidate the mechanisms of VA in ATS. We adopted a kinetic model of KCNJ2 in which channel block by Mg(+2) and spermine was incorporated. In this study, we attempt to examine the effects of KCNJ2 mutations on the ventricular action potential (AP), single-channel Markovian models were reformulated and incorporated into the dynamic Luo-Rudy model for rapidly and slowly delayed rectifying K(+) currents and KCNJ2 channel. During pacing at 1.0 Hz with [K(+)]o at 5.4 mM, a stepwise 10% reduction of Kir2.1 channel conductance progressively prolonged the terminal repolarization phase of AP along with gradual depolarization of the resting membrane potential (RMP). At 90% reduction, early after- depolarizations (EADs) became inducible and RMP was depolarized to -55.0 mV (control: -90.1 mV) followed by emergence of spontaneous action potentials (SAP). Both EADs and SAP were facilitated by a decrease in [K(+)]o and suppressed by increase in [K(+)]o. beta-adrenergic stimulation enhanced delayed after-depolarizations (DADs) and could also facilitate EADs as well as SAP in the setting of low [K(+)]o and reduced Kir2.1 channel conductance. In conclusion, the spectrum of VA in ATS includes (1) triggered activity mediated by EADs and/or DADs, and (2) abnormal automaticity manifested as SAP. These VA can be aggravated by a decrease in [K(+)]o and beta-adrenergic stimulation, and may potentially induce torsades de pointes and cause sudden death. In patients with ATS, the hypokalemic form of periodic paralysis should have the highest propensity to VA especially during physical activities.
165.  Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)
The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them.
166.  Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
"... The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. ..."
167.  STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)
"... We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. ..."
168.  STDP depends on dendritic synapse location (Letzkus et al. 2006)
This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. Created by B. Kampa (2006).
169.  Stochastic calcium mechanisms cause dendritic calcium spike variability (Anwar et al. 2013)
" ... In single Purkinje cells, spontaneous and synaptically evoked dendritic calcium bursts come in a variety of shapes with a variable number of spikes. The mechanisms causing this variability have never been investigated thoroughly. In this study, a detailed computational model employing novel simulation routines is applied to identify the roles that stochastic ion channels, spatial arrangements of ion channels and stochastic intracellular calcium have towards producing calcium burst variability. … Our findings suggest that stochastic intracellular calcium mechanisms play a crucial role in dendritic calcium spike generation and are, therefore, an essential consideration in studies of neuronal excitability and plasticity."
170.  Striatal D1R medium spiny neuron, including a subcellular DA cascade (Lindroos et al 2018)
We are investigating how dopaminergic modulation of single channels can be combined to make the D1R possitive MSN more excitable. We also connect multiple channels to substrates of a dopamine induced subcellular cascade to highlight that the classical pathway is too slow to explain DA induced kinetics in the subsecond range (Howe and Dombeck, 2016. doi: 10.1038/nature18942)
171.  Striatal Spiny Projection Neuron, inhibition enhances spatial specificity (Dorman et al 2018)
We use a computational model of a striatal spiny projection neuron to investigate dendritic spine calcium dynamics in response to spatiotemporal patterns of synaptic inputs. We show that spine calcium elevation is stimulus-specific, with supralinear calcium elevation in cooperatively stimulated spines. Intermediate calcium elevation occurs in neighboring non-stimulated dendritic spines, predicting heterosynaptic effects. Inhibitory synaptic inputs enhance the difference between peak calcium in stimulated spines, and peak calcium in non-stimulated spines, thereby enhancing stimulus specificity.
172.  Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)
" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD."
173.  Superior paraolivary nucleus neuron (Kopp-Scheinpflug et al. 2011)
This is a model of neurons in the brainstem superior paraolivary nucleus (SPN), which produce very salient offset firing during sound stimulation. Rebound offset firing is triggered by IPSPs coming from the medial nucleus of the trapezoid body (MNTB). This model shows that AP firing can emerge from inhibition through integration of large IPSPs, driven by an extremely negative chloride reversal potential, combined with a large hyperpolarization- activated non-specific cationic current (IH), with a secondary contribution from a T-type calcium conductance (ITCa). As a result, tiny gaps in sound stimuli of just 3-4ms can elicit reliable APs that signal such brief offsets.
174.  Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)
"We describe a new molecular mechanism of dopamine-induced membrane protein modulation that can tune neuronal oscillation frequency to attention related gamma rhythm. This mechanism is based on the unique ability of D4 dopamine receptors (D4R) to carry out phospholipid methylation (PLM) that may affect the kinetics of ion channels. We show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ..."
175.  T channel currents (Vitko et al 2005)
Computer simulations predict that seven of the SNPs would increase firing of neurons, with three of them inducing oscillations at similar frequencises. 3 representative models from the paper have been submited: a wild-type (WT) recombinant Cav3.2 T-channel, and two of the mutants described in the Vitko et al., 2005 paper (C456S and R788C). See the paper for more and details.
176.  T-type Ca current in thalamic neurons (Wang et al 1991)
A model of the transient, low-threshold voltage-dependent (T-type) Ca2+ current is constructed using whole-cell voltage-clamp data from enzymatically isolated rat thalamocortical relay neurons. The T-type Ca2+ current is described according to the Hodgkin-Huxley scheme, using the m3h format, with rate constants determined from the experimental data.
177.  T-type Calcium currents (McRory et al 2001)
NEURON mod files for CaT currents from the paper McRory et al., J.Biol.Chem. 276:3999 (2001). In this paper, three members (alpha-1G, -1H, and -1I) of the LVA calcium channels family were studied. Kinetic parameters were derived from functional expression in transfected cells.
178.  Thalamic interneuron multicompartment model (Zhu et al. 1999)
This is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. After playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in J Neurophysiol, 81:702--711, 1999, fig. 8b top trace. This sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out. Simulation by JJ Zhu To run nrnivmodl nrngui.hoc
179.  Thalamic neuron: Modeling rhythmic neuronal activity (Meuth et al. 2005)
The authors use an in vitro cell model of a single acutely isolated thalamic neuron in the NEURON simulation environment to address and discuss questions in an undergraduate course. Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix.
180.  Thalamic quiescence of spike and wave seizures (Lytton et al 1997)
A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).
181.  Thalamic Relay Neuron: I-T current (Williams, Stuart 2000)
NEURON mod files for the Ca-T current from the paper: Williams SR, Stuart GJ, Action potential backpropagation and somato-dendritic distribution of ion channels in thalamocortical neurons. J Neurosci. 2000 20:1307-17. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.
182.  Thalamic Reticular Network (Destexhe et al 1994)
Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)
183.  Thalamic reticular neurons: the role of Ca currents (Destexhe et al 1996)
The experiments and modeling reported in this paper show how intrinsic bursting properties of RE cells may be explained by dendritic calcium currents.
184.  Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)
"In Parkinson’s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations. These oscillatory activities are transmitted to the thalamic relay nuclei. Computer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ..."
185.  Thalamocortical loop with delay for investigation of absence epilepsy (Liu et al 2019)
Conductance based network model of one thalamic reticular neuron, one thalamic pyramidal neuron and one cortical pyramidal neuron. Used to show that large delay in the corticothalamic connection can lead to multistability.
186.  Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)
NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above)
187.  Thalamocortical augmenting response (Bazhenov et al 1998)
In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
188.  Thalamocortical model of spike and wave seizures (Suffczynski et al. 2004)
SIMULINK macroscopic model of transitions between normal (spindle) activity and spike and wave (SW) discharges in the thalamocortical network. The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model.
189.  Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018)
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices to adjust, fine-tune and validate a three-compartment TCR model cell (Destexhe et al. 1998, accession number 279). Three currents were added: an h-current (Destexhe et al. 1993,1996, accession number 3343), a high-threshold calcium current and a calcium- activated potassium current (Huguenard & McCormick 1994, accession number 3808). The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. Finally, the model was used to in the more realistic “high-conductance state” (Destexhe et al. 2001, accession number 8115), while being stimulated with a Poisson input (Brette et al. 2007, Vogels & Abbott 2005, accession number 83319), where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.
190.  Thalamocortical relay neuron models constrained by experiment and optimization (Iavarone et al 2019)
191.  The origin of different spike and wave-like events (Hall et al 2017)
Acute In vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike and wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalised and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden and apparent anatomical origin (thalamic, neocortical or both), but any relationship between this heterogeneity and underlying pathology remains elusive. Here we demonstrate that physiological delta frequency rhythms act as an effective substrate to permit modelling of SpW of cortical origin and may help to address this issue. ..."
192.  The subcellular distribution of T-type Ca2+ channels in LGN interneurons (Allken et al. 2014)
" ...To study the relationship between the (Ca2+ channel) T-distribution and several (LGN interneuron) IN response properties, we here run a series of simulations where we vary the T-distribution in a multicompartmental IN model with a realistic morphology. We find that the somatic response to somatic current injection is facilitated by a high T-channel density in the soma-region. Conversely, a high T-channel density in the distal dendritic region is found to facilitate dendritic signalling in both the outward direction (increases the response in distal dendrites to somatic input) and the inward direction (the soma responds stronger to distal synaptic input). ..."
193.  Theta phase precession in a model CA3 place cell (Baker and Olds 2007)
"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell’s place field. ... Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity."
194.  Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)
" ... Here we describe a simple method that allows us to investigate the existence and stability of anti-phase bursting solutions in a network of two spiking neurons, each possessing a T-type calcium current and coupled by reciprocal inhibition. We derive a one-dimensional map which fully characterizes the genesis and regulation of anti-phase bursting arising from the interaction of the T-current properties with the properties of synaptic inhibition. ..."
195.  Unbalanced peptidergic inhibition in superficial cortex underlies seizure activity (Hall et al 2015)
" ...Loss of tonic neuromodulatory excitation, mediated by nicotinic acetylcholine or serotonin (5HT3A) receptors, of 5HT3-immunopositive interneurons caused an increase in amplitude and slowing of the delta rhythm until each period became the "wave" component of the spike and wave discharge. As with the normal delta rhythm, the wave of a spike and wave discharge originated in cortical layer 5. In contrast, the "spike" component of the spike and wave discharge originated from a relative failure of fast inhibition in layers 2/3-switching pyramidal cell action potential outputs from single, sparse spiking during delta rhythms to brief, intense burst spiking, phase-locked to the field spike. The mechanisms underlying this loss of superficial layer fast inhibition, and a concomitant increase in slow inhibition, appeared to be precipitated by a loss of neuropeptide Y (NPY)-mediated local circuit inhibition and a subsequent increase in vasoactive intestinal peptide (VIP)-mediated disinhibition. Blockade of NPY Y1 receptors was sufficient to generate spike and wave discharges, whereas blockade of VIP receptors almost completely abolished this form of epileptiform activity. These data suggest that aberrant, activity-dependent neuropeptide corelease can have catastrophic effects on neocortical dynamics."
196.  Using Strahler`s analysis to reduce realistic models (Marasco et al, 2013)
Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs.
197.  Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018)
"Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo- cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. ..."
198.  VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)
In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials.

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