| | Models | Description |
| 1. |
3D model of the olfactory bulb (Migliore et al. 2014)
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This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper:
"Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd. |
| 2. |
A network model of tail withdrawal in Aplysia (White et al 1993)
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The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation.
A three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia.
Please see paper for more results and details. |
| 3. |
A network of AOB mitral cells that produces infra-slow bursting (Zylbertal et al. 2017)
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Infra-slow rhythmic neuronal activity with very long (> 10 s) period duration was described in many brain areas but little is known about the role of this activity and the mechanisms that produce it. Here we combine experimental and computational methods to show that synchronous infra-slow bursting activity in mitral cells of the mouse accessory olfactory bulb (AOB) emerges from interplay between intracellular dynamics and network connectivity. In this novel mechanism, slow intracellular Na+ dynamics endow AOB mitral cells with a weak tendency to burst, which is further enhanced and stabilized by chemical and electrical synapses between them. Combined with the unique topology of the AOB network, infra-slow bursting enables integration and binding of multiple chemosensory stimuli over prolonged time scale.
The example protocol simulates a two-glomeruli network with a single shared cell. Although each glomerulus is stimulated at a different time point, the activity of the entire population becomes synchronous (see paper Fig. 8) |
| 4. |
A single column thalamocortical network model (Traub et al 2005)
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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 spatial model of the intermediate superior colliculus (Moren et. al. 2013)
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A spatial model of the intermediate superior colliculus. It reproduces the collicular saccade-generating output profile from NMDA receptor-driven burst neurons, shaped by integrative inhibitory feedback from spreading buildup neuron activity. The model is consistent with the view that collicular activity directly shapes the temporal profile of saccadic eye movements.
We use the Adaptive exponential integrate and fire neuron model, augmented with an NMDA-like membrane potential-dependent receptor. In addition, we use a synthetic spike integrator model as a stand-in for a spike-integrator circuit in the reticular formation.
NOTE: We use a couple of custom neuron models, so the supplied model file includes an entire version of NEST. I also include a patch that applies to a clean version of the simulator (see the doc/README). |
| 6. |
Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)
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Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article
Instructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include "ModelDB - BGnet" in the subject heading. |
| 7. |
Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)
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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. |
| 8. |
Bursting and oscillations in RD1 Retina driven by AII Amacrine Neuron (Choi et al. 2014)
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"In many forms of retinal degeneration, photoreceptors die but inner retinal circuits remain intact. In the rd1 mouse, an established model for blinding retinal diseases, spontaneous activity in the coupled network of AII amacrine and ON cone bipolar cells leads to rhythmic bursting of ganglion cells. Since such activity could impair retinal and/or cortical responses to restored photoreceptor function, understanding its nature is important for developing treatments of retinal pathologies. Here we analyzed a compartmental model of the wild-type mouse AII amacrine cell to predict that the cell's intrinsic membrane properties, specifically, interacting fast Na and slow, M-type K conductances, would allow its membrane potential to oscillate when light-evoked excitatory synaptic inputs were withdrawn following photoreceptor degeneration. ..." |
| 9. |
Bursting respiratory net: clustered architecture gives large phase diff`s (Fietkiewicz et al 2011)
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Using a previous model of respiratory rhythm generation, we modified the network architecture such that cells can be segregated into two clusters. Cells within a given cluster burst with smaller phase differences than do cells from different clusters. This may explain the large phase differences seen experimentally, as reported in the paper. |
| 10. |
CA1 pyramidal neuron network model (Ferguson et al 2015)
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From the paper:
Figure 4 (1000 cell network) is reproduced by running this brian code.
The raster plot and one of the excitatory cell voltage is produced. |
| 11. |
Ca2+-activated I_CAN and synaptic depression promotes network-dependent oscil. (Rubin et al. 2009)
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"... the preBotzinger complex...
we present and analyze a
mathematical model demonstrating an unconventional mechanism
of rhythm generation in which glutamatergic synapses and the
short-term depression of excitatory transmission play key rhythmogenic
roles.
Recurrent synaptic excitation triggers postsynaptic Ca2+-
activated nonspecific cation current (ICAN) to initiate a network-wide
burst.
Robust depolarization due to ICAN also causes voltage-dependent
spike inactivation, which diminishes recurrent excitation and
thus attenuates postsynaptic Ca2+ accumulation.
..." |
| 12. |
Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
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The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior.
A model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli.
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| 13. |
Classic model of the Tritonia Swim CPG (Getting, 1989)
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Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible. |
| 14. |
Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
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"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."
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| 15. |
Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab)
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Excerpts from three papers abstracts: "Brain neurons exhibit complex electroresponsive properties – including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset – which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. ..." "... In order to reproduce these properties in single-point neuron models, we have optimized the Extended-Generalized Leaky Integrate and Fire (E-GLIF) neuron through a multi-objective gradient-based algorithm targeting the desired input–output relationships. ..." " ... In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). ..." |
| 16. |
Computer model of clonazepam's effect in thalamic slice (Lytton 1997)
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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. |
| 17. |
Dynamic cortical interlaminar interactions (Carracedo et al. 2013)
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"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta
and nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness."
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| 18. |
Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)
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" ... We use the lamprey as a model for investigating the role of this
phasic modulation of the reticulospinal activity, because the
brainstem–spinal cord networks are known down to the cellular level in
this phylogenetically oldest extant vertebrate.
We describe how the phasic modulation of reticulospinal activity from
the spinal CPG ensures reliable steering/turning commands without the
need for a very precise timing of on- or offset, by using a
biophysically detailed large-scale (19,600 model neurons and 646,800
synapses) computational model of the lamprey brainstem–spinal cord
network.
To verify that the simulated neural network can control body
movements, including turning, the spinal activity is fed to a
mechanical model of lamprey swimming.
..." |
| 19. |
Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)
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Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the tectal layer 10 (L10) neurons and respond with oscillatory bursts to visual stimulation. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation. |
| 20. |
Half-center oscillator database of leech heart interneuron model (Doloc-Mihu & Calabrese 2011)
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We have created a database (HCO-db) of instances of a half-center oscillator computational model [Hill et al., 2001] for analyzing how neuronal parameters influence network activity. We systematically explored the parameter space of about 10.4 million simulated HCO instances and corresponding isolated neuron model simulations obtained by varying a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations using a brute-force approach. We classified these HCO instances by their activity characteristics into identifiable groups. We built an efficient relational database table (HCO-db) with the resulting instances characteristics. |
| 21. |
Homeostatic mechanisms may shape oscillatory modulations (Peterson & Voytek 2020)
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"Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca2+-dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-don't provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response, and so can preserve the pyramidal cell's natural dynamic range. Based on these results we can also speculate that homeostasis may explain why AMPAergic oscillations in cortex, and hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant, and so retain their normal excitatory effect." |
| 22. |
Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
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"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K+]o) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled ..." |
| 23. |
Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994)
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Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements. |
| 24. |
Large scale model of the olfactory bulb (Yu et al., 2013)
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The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
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| 25. |
Leech Heart (HE) Motor Neuron conductances contributions to NN activity (Lamb & Calabrese 2013)
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"...
To explore the relationship between conductances,
and in particular how they influence the activity of motor neurons in
the well characterized leech heartbeat system, we developed a new
multi-compartmental Hodgkin-Huxley style leech heart motor neuron
model.
To do so, we evolved a population of model instances, which
differed in the density of specific conductances, capable of achieving
specific output activity targets given an associated input pattern.
...
We found that the strengths of many conductances,
including those with differing dynamics, had strong partial
correlations and that these relationships appeared to be linked by
their influence on heart motor neuron activity.
Conductances that had
positive correlations opposed one another and had the opposite effects
on activity metrics when perturbed whereas conductances that had
negative correlations could compensate for one another and had similar
effects on activity metrics.
" |
| 26. |
Leech heart interneuron network model (Hill et al 2001, 2002)
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We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. In the intact nerve cord, segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. See references for more details. Biologically realistic network models with two, six, and eight cells and a tutorial are available at the links to Calabrese's web site below. |
| 27. |
Linking dynamics of the inhibitory network to the input structure (Komarov & Bazhenov 2016)
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Code to model 10 all-to-all coupled inhibitory neurons. |
| 28. |
Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004)
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This pyloric network model simulator is a C/C++ program that saves 384 different calcium sensor values that are candidates for activity sensors (Gunay and Prinz, 2010). The simulator was used to scan all of the 20 million pyloric network models that were previously collected in a database (Prinz et al, 2004). |
| 29. |
Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)
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To explore interactions between respiration, inhibition, and olfaction,
experiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico.
This archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses. |
| 30. |
Multiscale model of excitotoxicity in PD (Muddapu and Chakravarthy 2020)
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Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in Substantia Nigra pars compacta (SNc). Although the exact cause of cell death is not clear, the hypothesis that metabolic deficiency is a key factor has been gaining attention in recent years. In the present study, we investigate this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and SNc. The proposed model is a multiscale model in that interactions among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slows down the progression of SNc cell loss. |
| 31. |
Multiscale modeling of epileptic seizures (Naze et al. 2015)
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" ... In the context of epilepsy, the functional properties of the network at the source of a seizure are disrupted by a possibly large set of factors at the cellular and molecular levels. It is therefore needed to sacrifice some biological accuracy to model seizure dynamics in favor of macroscopic realizations. Here, we present a neuronal network model that convenes both neuronal and network representations with the goal to describe brain dynamics involved in the development of epilepsy. We compare our modeling results with animal in vivo recordings to validate our approach in the context of seizures. ..." |
| 32. |
Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
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It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed “network spikes” (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally. |
| 33. |
Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)
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Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input. |
| 34. |
Norns - Neural Network Studio (Visser & Van Gils 2014)
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The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts:
1. "Urd": a Matlab frontend with high-level functions for quickly defining networks
2. "Verdandi": an optimized C++ simulation environment which runs the simulation defined by Urd
3. "Skuld": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data. |
| 35. |
Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012)
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In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous. |
| 36. |
Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)
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The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.
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| 37. |
Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)
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"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience." |
| 38. |
Purkinje cell: Synaptic activation predicts voltage control of burst-pause (Masoli & D'Angelo 2017)
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"The dendritic processing in cerebellar Purkinje cells (PCs), which integrate synaptic inputs coming from hundreds of thousands granule cells and molecular layer interneurons, is still unclear. Here we have tested a leading hypothesis maintaining that the significant PC output code is represented by burst-pause responses (BPRs), by simulating PC responses in a biophysically detailed model that allowed to systematically explore a broad range of input patterns. BPRs were generated by input bursts and were more prominent in Zebrin positive than Zebrin negative (Z+ and Z-) PCs. Different combinations of parallel fiber and molecular layer interneuron synapses explained type I, II and III responses observed in vivo. BPRs were generated intrinsically by Ca-dependent K channel activation in the somato-dendritic compartment and the pause was reinforced by molecular layer interneuron inhibition. BPRs faithfully reported the duration and intensity of synaptic inputs, such that synaptic conductance tuned the number of spikes and release probability tuned their regularity in the millisecond range. ..." |
| 39. |
Reverberatory bursts propagation and synchronization in developing cultured NNs (Huang et al 2016)
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"Developing networks of neural systems can exhibit spontaneous,
synchronous activities called neural bursts, which can be important in
the organization of functional neural circuits.
...
Using a propagation model we infer the spreading
speed of the spiking activity, which increases as the culture ages.
We
perform computer simulations of the system using a physiological model
of spiking networks in two spatial dimensions and find the parameters
that reproduce the observed resynchronization of spiking in the
bursts.
An analysis of the simulated dynamics suggests that the
depletion of synaptic resources causes the resynchronization.
The spatial propagation dynamics of the simulations match well with
observations over the course of a burst and point to an interplay of
the synaptic efficacy and the noisy neural self-activation in
producing the morphology of the bursts." |
| 40. |
Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
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This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them. |
| 41. |
Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)
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Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field. |
| 42. |
Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)
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This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis. |
| 43. |
The activity phase of postsynaptic neurons (Bose et al 2004)
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We show, in a simplified network consisting of an oscillator
inhibiting a follower neuron, how the interaction between synaptic depression
and a transient potassium current in the follower neuron determines the
activity phase of this neuron. We derive a mathematical expression to
determine at what phase of the oscillation the follower neuron becomes
active. This expression can be used to understand which parameters determine
the phase of activity of the follower as the frequency of the oscillator is
changed. See paper for more. |
| 44. |
Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)
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" ... 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. ..." |
| 45. |
Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)
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Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. |
