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(Activated by depolarization above -65 mV; slow, weak and non-inactivating; blocked by ligands like acetylcholine acting through muscarinic (M) receptors)
| Models | Description |
| A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011) | |
| We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper. | |
| A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012) | |
| These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at www.g-node.org/emoo and in Bahl et al. (2012). | |
| A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2010) | |
| These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew. The files reproduce Figures 2c-f from Brown et al, 2010 "Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling". | |
| 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. | |
| 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. | |
| 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. | |
| AP initiation and propagation in type II cochlear ganglion cell (Hossain et al 2005) | |
| The model of type II cochlear ganglion cell was based on the immunostaining of the mouse auditory pathway. Specific antibodies were used to map the distribution of voltage-dependent sodium channels along the two unmyelinated axon-like processes of the bipolar ganglion cells. Three distinct hot spots were detected. A high density of sodium channels was present over the entire trajectory of sensory endings beneath the outer hair cells (the most distal portion of the peripheral axon). THE other two hot spots were localized in the initial segments of both of the axons that flank the unmyelinated bipolar ganglion cell bodies. A biophysical model indicates that all three hot spots might play important roles in action potential initiation and propagation. For instance, the hot spot in the receptor segment is important for transforming the receptor potentials into a full blown action potential (Supplemental Fig. 1). The hot spots in the two paraganglionic axon initial segments are there to ensure the successful propagation of action potentials from the peripheral to the central axon through the cell body. The Readme.txt file provides step by step instructions on how to recreate Figures 6 and 7 of Hossain et al., 2005 paper. | |
| Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009) | |
| "...Previous computational studies have yielded conflicting conclusions about the role of Na+ channel density and biophysical properties in action potential initiation as a result of inconsistent estimates of channel density. Our modeling studies integrated the immunostaining and electrophysiological results and showed that the lowest threshold for action potential initiation at the distal AIS was largely determined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel may control action potential backpropagation because of its high density at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines the low threshold for action potential initiation; whereas proximal AIS accumulation of Nav1.2 channels sets the threshold for the generation of somatodendritic potentials and ensures action potential backpropagation to the soma and dendrites. Thus, Nav1.6 and Nav1.2 channels serve distinct functions in action potential initiation and backpropagation." | |
| Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013) | |
| The role of amyloid beta (Aß) in brain function and in the pathogenesis of Alzheimer’s disease remains elusive. Recent publications reported that an increase in Aß concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased Aß can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer’s disease. | |
| 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." | |
| Axonal Projection and Interneuron Types (Helmstaedter et al. 2008) | |
| "Interneurons in layer 2/3 (L2/3) of the somatosensory cortex show 4 types of axonal projection patterns with reference to the laminae and borders of columns in rat barrel cortex (Helmstaedter et al. 2008a). Here, we analyzed the dendritic geometry and electrical excitability of these interneurons. ... We conclude that 1) dendritic polarity is correlated to intrinsic electrical excitability, and 2) the axonal projection pattern represents an independent classifier of interneurons. " | |
| 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 in press. | |
| Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006) | |
| "Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. ... A simplified phenomenological model is also derived..." | |
| Bursting and resonance in cerebellar granule cells (D'Angelo et al 2001) | |
| In this study we report theta-frequency (3–12 Hz) bursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more. | |
| CA1 Pyramidal Neuron: slow Na+ inactivation (Migliore 1996) | |
| Model files from the paper: M. Migliore, Modeling the attenuation and failure of action potentials in the dendrites of hippocampal neurons, Biophys. J. 71:2394-403 (1996). Please see the below readme file for installation and use instructions. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model. | |
| CA1 pyramidal cell: I_NaP and I_M contributions to somatic bursting (Golomb et al 2006) | |
| To study the mechanisms of bursting, we have constructed a conductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model, reduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current (INaP), as indicated by recent experimental results. The neuron model accounts, with different parameter sets, for the diversity of firing patterns observed experimentally in both zero and normal [Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes within a burst, but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis and bifurcation theory, that the M-type K+ current (IM) allows bursting by shifting neuronal behavior between a silent and a tonically-active state, provided the kinetics of the spike generating currents are sufficiently, though not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be explained by a single compartment *square bursting* mechanism with one slow variable, the activation of IM. See paper for more and details. | |
| CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011) | |
| Model of pyramidal CA1 cells connected by gap junctions in their axons. Cell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor. Model init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction. Model init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak. The paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus. | |
| CA1 pyramidal neuron to study INaP properties and repetitive firing (Uebachs et al. 2010) | |
| A model of a CA1 pyramidal neuron containing a biophysically realistic morphology and 15 distributed voltage and Ca2+-dependent conductances. Repetitive firing is modulated by maximal conductance and the voltage dependence of the persistent Na+ current (INaP). | |
| 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. | |
| CA1 pyramidal neuron: Ih current (Migliore et al. 2012) | |
| NEURON files from the paper: Migliore M, Migliore R (2012) Know Your Current Ih: Interaction with a Shunting Current Explains the Puzzling Effects of Its Pharmacological or Pathological Modulations. PLoS ONE 7(5): e36867. doi:10.1371/journal.pone.0036867. Experimental findings on the effects of Ih current modulation, which is particularly involved in epilepsy, appear to be inconsistent. In the paper, using a realistic model we show how and why a shunting current, such as that carried by TASK-like channels, dependent on the Ih peak conductance is able to explain virtually all experimental findings on Ih up- or down-regulation by modulators or pathological conditions. | |
| CA1 pyramidal neuron: depolarization block (Bianchi et al. 2012) | |
| NEURON files from the paper: On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons by D.Bianchi, A. Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y. Experimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block. We analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment. The results show the specic ion channel properties and kinetics that are needed to reproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block. | |
| CA1 pyramidal neuron: effects of R213Q and R312W Kv7.2 mutations (Miceli et al. 2013) | |
| NEURON mod files from the paper: Miceli et al, Genotype–phenotype correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits, PNAS 2013 Feb 25. [Epub ahead of print] In this paper, functional studies revealed that in homomeric or heteromeric configuration with KV7.2 and/or KV7.3 subunits, R213W and R213Q mutations markedly destabilized the open state, causing a dramatic decrease in channel voltage sensitivity. Modeling these channels in CA1 hippocampal pyramidal cells revealed that both mutations increased cell firing frequency, with the R213Q mutation prompting more dramatic functional changes compared with the R213W mutation. | |
| 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. | |
| 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. | |
| CA1 pyramidal neurons: effects of Kv7 (M-) channels on synaptic integration (Shah et al. 2011) | |
| NEURON mod files from the paper: Shah et al., 2011. In this study, using a combination of electrophysiology and computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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." | |
| 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. | |
| 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. | |
| 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. | |
| Computational aspects of feedback in neural circuits (Maass et al 2006) | |
| It had previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate ... the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceiv- able digital or analog computation on time-varying inputs. But even with noise the resulting computational model can perform a large class of biologically relevant real-time computations that require a non-fading memory. ... In particular we show that ... generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. See paper for more and details. | |
| Control of vibrissa motoneuron firing (Harish and Golomb 2010) | |
| We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium and h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly locked to the CPG input. | |
| Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007) | |
| In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present a simple numerical algorithm that corrects such distortions. The method enabled accurate retrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal neurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated currents in structures of any morphology that is amenable to the voltage-clamp technique. | |
| Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008) | |
| This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are a) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough). b) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents. c) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect. d) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008) | |
| Dendritica (Vetter et al 2001) | |
| Dendritica is a collection of programs for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in: Vetter, P., Roth, A. & Hausser, M. (2001) For reprint requests and additional information please contact Dr. M. Hausser, email address: m.hausser@ucl.ac.uk | |
| 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. ..." | |
| Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001) | |
| This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically. Some of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the presence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity. For more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr | |
| Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python) | |
| The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons). | |
| Hodgkin-Huxley model of persistent activity in prefrontal cortex neurons (Winograd et al. 2008) | |
| The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons). | |
| 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. ..." | |
| 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. | |
| 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. | |
| Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010) | |
| The code provided here was written to systematically investigate which of the
physical parameters controlled by dendritic morphology underlies the differences
in spiking behaviour observed in different realizations of the
'ping-pong'-model. Structurally varying dendritic topology and length in a
simplified model allows us to separate out the physical parameters derived from
morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes. | |
| Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007) | |
| " ... Here we show that the voltage fluctuations associated with dendrosomatic synaptic activity propagate significant distances along the axon, and that modest changes in the somatic membrane potential of the presynaptic neuron modulate the amplitude and duration of axonal action potentials and, through a Ca21- dependent mechanism, the average amplitude of the postsynaptic potential evoked by these spikes. These results indicate that synaptic activity in the dendrite and soma controls not only the pattern of action potentials generated, but also the amplitude of the synaptic potentials that these action potentials initiate in local cortical circuits, resulting in synaptic transmission that is a mixture of triggered and graded (analogue) signals." | |
| 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." | |
| 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. " | |
| 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." | |
| Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005) | |
| "We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms. The model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels. Model parameters are calibrated using physiological data. Computer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation. We find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels." | |
| Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004) | |
| θ Frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes. ... In the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in θ oscillation. ... In parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: http://geza.kzoo.edu/theta). This connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors. In addition, computational findings indicated different roles of distinctively located GABAA receptors in θ generation. | |
| Multicompartmental cerebellar granule cell model (Diwakar et al. 2009) | |
| A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article. | |
| 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. For more information see http://www.cnl.salk.edu/Simulations. Salk Institute, Computational Neurobiology Lab, 10010 North Torrey Pines Rd., La Jolla CA 092037. Email: arthur@salk.edu | |
| Na+ channel dependence of AP initiation in cortical pyramidal neuron (Kole et al. 2008) | |
| In this simulation action potential initiation, action potential properties and the role of axon initial segment Na+ channels are investigated in a realistic model of a layer 5 pyramidal neuron axon initial segment. The main Na+ channel properties were constrained by experimental data and the axon initial segment was reconstructed. Model parameters were constrained by direct recordings at the axon initial segment. | |
| 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. | |
| Olfactory Bulb Network (Davison et al 2003) | |
| A biologically-detailed model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. For more details, see the reference below. | |
| 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. ..." | |
| 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. | |
| Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005) | |
| The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations. | |
| Phase response curve of a globus pallidal neuron (Fujita et al. 2011) | |
| We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current. | |
| Pyramidal Neuron Deep, Superficial; Aspiny, Stellate (Mainen and Sejnowski 1996) | |
| This package contains compartmental models of four reconstructed neocortical neurons (layer 3 Aspiny, layer 4 Stellate, layer 3 and layer 5 Pyramidal neurons) with active dendritic currents using NEURON. Running this simulation demonstrates that an entire spectrum of firing patterns can be reproduced in this set of model neurons which share a common distribution of ion channels and differ only in their dendritic geometry. The reference paper is: Z. F. Mainen and T. J. Sejnowski (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382: 363-366. See also http://www.cnl.salk.edu/~zach/methods.html and http://www.cnl.salk.edu/~zach/ More info in readme.txt file below made visible by clicking on the patdemo folder and then on the readme.txt file. | |
| 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..." | |
| 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 | |
| Pyramidal neuron coincidence detection tuned by dendritic branching pattern (Schaefer et al 2006) | |
| "... We examined the relationship between dendritic arborization and the coupling between somatic and dendritic action potential (AP) initiation sites in layer 5 (L5) neocortical pyramidal neurons. Coupling was defined as the relative reduction in threshold for initiation of a dendritic calcium AP due to a coincident back-propagating AP. Simulations based on reconstructions of biocytin-filled cells showed that addition of oblique branches of the main apical dendrite in close proximity to the soma (d < 140 um) increases the coupling between the apical and axosomatic AP initiation zones, whereas incorporation of distal branches decreases coupling. ... We conclude that variation in dendritic arborization may be a key determinant of variability in coupling (49+-17%; range 19-83%; n = 37) and is likely to outweigh the contribution made by variations in active membrane properties. Thus coincidence detection of inputs arriving from different cortical layers is strongly regulated by differences in dendritic arborization." | |
| Pyramidal neurons switch from integrators to resonators (Prescott et al. 2008) | |
| During wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic input that causes tonic depolarization, increased membrane conductance (i.e. shunting), and noisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices typically experience little background input. Such differences in operating conditions can compromise extrapolation of in vitro data to explain neuronal operation in vivo. ... in slice experiments, we show that CA1 hippocampal pyramidal cells switch from integrators to resonators, i.e. from class 1 to class 2 excitability. The switch is explained by increased outward current contributed by the M-type potassium current IM ... Thus, even so-called “intrinsic” properties may differ qualitatively between in vitro and in vivo conditions. | |
| Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010) | |
| Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator. | |
| Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011) | |
| The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells. | |
| Regulation of KCNQ2/KCNQ3 current by G protein cycling (Suh et al 2004) | |
| Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns the kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). ... observations were successfully described by a kinetic model representing biochemical steps of the signaling cascade using published rate constants where available. The model supports the following sequence of events for this Gq-coupled signaling: A classical G-protein cycle, including competition for nucleotide-free G-protein by all nucleotide forms and an activation step requiring Mg2, followed by G-protein-stimulated phospholipase C and hydrolysis of PIP2, and finally PIP2 dissociation from binding sites for inositol lipid on the channels so that KCNQ current was suppressed. See paper for details and more. | |
| 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). | |
| 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. | |
| Sympathetic neuron (Wheeler et al 2004) | |
| This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. See references for details. | |
| 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." | |
| Tight junction model of CNS myelinated axons (Devaux and Gow 2008) | |
| Two models are included: 1) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in parallel with the myelin membrane RC circuit (called double cable model, DCM). 2) a myelinated axon is represented by an equivalent circuit with a double cable design but includes a tight junction in series with the myelin RC circuit (called tight junction model, TJM). These models have been used to simulate data from compound action potentials measured in mouse optic nerve from Claudin 11-null mice in Fig. 6 of: Devaux, J.J. & Gow, A. (2008) Tight Junctions Potentiate The Insulative Properties Of Small CNS Myelinated Axons. J Cell Biol 183, 909-921. | |
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