| | Models | Description |
| 1. |
A full-scale cortical microcircuit spiking network model (Shimoura et al 2018)
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Reimplementation in BRIAN 2 simulator of a full-scale cortical microcircuit containing two cell types (excitatory and inhibitory) distributed in four layers, and represents the cortical network below a surface of 1 mm² (Potjans & Diesmann, 2014). |
| 2. |
A model of neuronal bursting using three coupled first order diff. eqs. (Hindmarsh & Rose 1984)
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R Brette's Brian 2 implementation of the classic Hindmarsh-Rose 1984 dynamical system representing neuronal bursting. |
| 3. |
CN bushy, stellate neurons (Rothman, Manis 2003) (Brian 2)
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This model is an updated version of Romain Brette's adaptation of Rothman & Manis (2003). The model now uses Brian 2 instead of Brian 1 and can be configured to use n cells instead of a single cell. The included figure shows that Brian 2 is more efficient than Brian 1 once the number of cells exceeds 1,000. |
| 4. |
eLIF and mAdExp: energy-based integrate-and-fire neurons (Fardet and Levina 2020)
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The eLIF and mAdExp neurons respectively extend the leaky integrate-and-fire and adaptive exponential (AdExp) neuron models.
They include a new variable modelling the availability of energy substrate and model constraints that energy availability may have on the subthreshold and spiking dynamics.
In the paper, we show how these models can reproduce complex dynamics and prove especially useful to model metabolic disruption, for instance in large-scale models of epilepsy or other diseases with metabolic components, such as Alzheimer, or Parkinson.
Git repository: https://git.sr.ht/~tfardet/elif-madexp |
| 5. |
Excitation Properties of Computational Models of Unmyelinated Peripheral Axons (Pelot et al., 2021)
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We implemented the single-compartment model of vagal afferents from Schild et al. 1994 and extended the model into a multi-compartment axon, presenting the first C-fiber cable model of a C-fiber vagal afferent. We also implemented the updated parameters from Schild and Kunze 1997. We compared the responses of these novel models to three published models of unmyelinated axons (Rattay and Aberham 1993; Sundt et al. 2015; Tigerholm et al. 2014). |
| 6. |
Healthy and Epileptic Hippocampal Circuit (Aussel et al 2022)
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This model aims at reproducing healthy and epileptic hippocampal oscillations, and includes modeling of the sleep-wake cycle. It was used to study theta-nested gamma oscillations, sharp-wave ripple complexes, |
| 7. |
Inhibitory microcircuits for top-down plasticity of sensory representations (Wilmes & Clopath 2019)
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"Rewards influence plasticity of early sensory representations, but the underlying changes in circuitry are unclear. Recent experimental findings suggest that inhibitory circuits regulate learning. In addition, inhibitory neurons are highly modulated by diverse long-range inputs, including reward signals. We, therefore, hypothesise that inhibitory plasticity plays a major role in adjusting stimulus representations. We investigate how top-down modulation by rewards interacts with local plasticity to induce long-lasting changes in circuitry. Using a computational model of layer 2/3 primary visual cortex, we demonstrate how interneuron circuits can store information about rewarded stimuli to instruct long-term changes in excitatory connectivity in the absence of further reward. In our model, stimulus-tuned somatostatin-positive interneurons develop strong connections to parvalbumin-positive interneurons during reward such that they selectively disinhibit the pyramidal layer henceforth. This triggers excitatory plasticity, leading to increased stimulus representation. We make specific testable predictions and show that this two-stage model allows for translation invariance of the learned representation." |
| 8. |
LIP and FEF rhythmic attention model (Aussel et al. 2023)
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This model investigates how theta-rhythmic performance in an attentional task can emerge from the dynamics of the Lateral IntraParietal area (LIP) and the Frontal Eye Fields (FEF) when stimulated by the medial-dorsal pulvinar. |
| 9. |
Low Threshold Calcium Currents in TC cells (Destexhe et al 1998) (Brian)
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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)! |
| 10. |
MEC PV-positive fast-spiking interneuron network generates theta-nested fast oscillations
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We use a computational model of a network of Fast-Spiking Parvalbumin-positive Basket Cells to study its synchronizing properties. The intrinsic properties of neurons, properties of chemical synapses and of gap junctions are calibrated using electrophysiological recordings in mice Medial Entorhinal Cortex slices. The neurons synchronize, generating Fast Oscillations nested in an external theta drive. We show how gap junctions are necessary for the generation of the oscillations, how hyperpolarizing chemical synapses give rise to more robust fast oscillations, compared to shunting ones, and how short-term depression in the chemical synapses confine the fast oscillation on a narrow range of phases from the external theta drive. |
| 11. |
Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018)
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We present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We use a Master Equation formalism, together with a semi-analytic approach to the transfer function of AdEx neurons to describe the average dynamics of the coupled populations. We compare the predictions of this mean-field model to simulated networks of RS-FS cells, first at the level of the spontaneous activity of the network, which is well predicted by the analytical description. Second, we investigate the response of the network to time-varying external input, and show that the mean-field model predicts the response time course of the population. Finally, to model VSDi signals, we consider a one-dimensional ring model made of interconnected RS-FS mean-field units. |
| 12. |
Model of the hippocampus over the sleep-wake cycle using Hodgkin-Huxley neurons (Aussel et al 2018)
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" ...we propose a
computational model of the hippocampal formation based on a
realistic topology and synaptic connectivity, and we analyze the
effect of different changes on the network, namely the variation
of synaptic conductances, the variations of the CAN channel
conductance and the variation of inputs. By using a detailed
simulation of intracerebral recordings, we show that this is able
to reproduce both the theta-nested gamma oscillations that are
seen in awake brains and the sharp-wave ripple complexes measured
during slow-wave sleep. The results of our simulations support
the idea that the functional connectivity of the hippocampus,
modulated by the sleep-wake variations in Acetylcholine
concentration, is a key factor in controlling its rhythms." |
| 13. |
Modeling dendritic spikes and plasticity (Bono and Clopath 2017)
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Biophysical model and reduced neuron model with voltage-dependent plasticity. |
| 14. |
Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)
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"To understand how neurons and nervous systems first evolved, we need
an account of the origins of neural elongations: why did neural
elongations (axons and dendrites) first originate, such that they
could become the central component of both neurons and nervous
systems? Two contrasting conceptual accounts provide different answers
to this question. Braitenberg's vehicles provide the iconic
illustration of the dominant input-output (IO) view. Here, the basic
role of neural elongations is to connect sensors to effectors, both
situated at different positions within the body. For this function,
neural elongations are thought of as comparatively long and specific
connections, which require an articulated body involving substantial
developmental processes to build. Internal coordination (IC) models
stress a different function for early nervous systems. Here, the
coordination of activity across extended parts of a multicellular body
is held central, in particular, for the contractions of (muscle)
tissue. An IC perspective allows the hypothesis that the earliest
proto-neural elongations could have been functional even when they
were initially simple, short and random connections, as long as they
enhanced the patterning of contractile activity across a multicellular
surface. The present computational study provides a proof of concept
that such short and random neural elongations can play this
role. ..." |
| 15. |
Neuromuscular network model of gut motility (Barth et al 2017)
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Here we develop an integrated neuromechanical model of the ENS and assess neurostimulation strategies for enhancing gut motility. The model includes a network of enteric neurons, smooth muscle fibers, and interstitial cells of Cajal, which regulate propulsion of a virtual pellet in a model of gut motility. |
| 16. |
Physiological noise facilitates multiplexed coding of vibrotactile signals in somatosensory cortex
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Simulations were conducted using a modified AdEx model. All simulations were performed in Brian2 with the Euler-Maruyama algorithm with fixed time step of 10 µs . |
| 17. |
PING, ING and CHING network models for Gamma oscillations in cortex (Susin and Destexhe 2021)
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These models were published at:
Susin E, Destexhe A. 2021. Integration, coincidence detection and resonance in networks of spiking neurons expressing gamma oscillations and asynchronous states. bioRxiv doi: 10.1101/2021.05.03.442436
In this article, we constructed conductance-based network models of gamma oscillations, based on different cell types found in cerebral cortex: Regular Spiking (RS), Fast Spiking (FS) and Chattering cells. The models were adjusted to extracellular unit recordings in humans, where gamma oscillations always coexist with the asynchronous firing mode. We considered three different mechanisms to generate Gamma, first a mechanism based on the interaction between pyramidal neurons and interneurons (PING), second a mechanism in which gamma is generated in interneuron networks (ING) and third, a mechanism which relies on gamma oscillations generated by pacemaker Chattering neurons (CHING). We found that in all cases, the presence of Gamma oscillations tends to diminish the responsiveness of the networks to external inputs. We tested different paradigms and found none in which Gamma oscillations would favor information flow compared to asynchronous states. |
| 18. |
PLS-framework (Tikidji-Hamburyan and Colonnese 2021)
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"Numerical simulations become incredibly challenging when an extensive network with a detailed representation of each neuron needs to be modeled over a long time interval to study slow evolving processes, e.g. development of the thalamocortical circuits. Here we suggest a simple, powerful and flexible approach in which we approximate the right-hand sides of differential equations by combinations of functions from three families: Polynomial, piecewise-Linear, Step (PLS). To obtain a single coherent framework, we provide four core principles in which PLS functions should be combined. We show the rationale behind each of the core principles. Two examples illustrate how to build a conductance-based or phenomenological model using the PLS-framework. We use the first example as a benchmark on three different computational platforms: CPU, GPU, and mobile system-on-chip devices." |
| 19. |
PyRhO: A multiscale optogenetics simulation platform (Evans et al 2016)
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"... we present an integrated suite of open-source, multi-scale
computational tools called PyRhO. The purpose of developing PyRhO is
three-fold: (i) to characterize new (and existing) opsins by
automatically fitting a minimal set of experimental data to three-,
four-, or six-state kinetic models, (ii) to simulate these models at
the channel, neuron and network levels, and (iii) provide functional
insights through model selection and virtual experiments in
silico. The module is written in Python with an additional
IPython/Jupyter notebook based GUI, allowing models to be fit,
simulations to be run and results to be shared through simply
interacting with a webpage. The seamless integration of model fitting
algorithms with simulation environments (including NEURON and Brian2)
for these virtual opsins will enable neuroscientists to gain a
comprehensive understanding of their behavior and rapidly identify the
most suitable variant for application in a particular biological
system. ..." |
| 20. |
Response to correlated synaptic input for HH/IF point neuron vs with dendrite (Górski et al 2018)
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" ... Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons." |
| 21. |
Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)
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"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks." |
| 22. |
Sharpness of spike initiation in neurons explained by compartmentalization (Brette 2013)
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"Spike initiation determines how the combined inputs to a neuron are converted to an output. Since the pioneering work of Hodgkin and Huxley, it is known that spikes are generated by the opening of sodium channels with depolarization. According to this standard theory, these channels should open gradually when the membrane potential increases, but spikes measured at the soma appear to suddenly rise from rest. This apparent contradiction has triggered a controversy about the origin of spike “sharpness.” This study shows with biophysical modelling that if sodium channels are placed in the axon rather than in the soma, they open all at once when the somatic membrane potential exceeds a critical value. This work explains the sharpness of spike initiation and provides another demonstration that morphology plays a critical role in neural function." |
| 23. |
Single neuron models of four types of L1 mouse Interneurons: Canpy, NGFC, alpha7 and VIP cells
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Neocortical Layer 1 (L1) consists of the distal dendrites of pyramidal cells and GABAergic interneurons (INs) and receives extensive long-range “top-down” projections, but L1 INs remain poorly understood. In this work, we systematically examined the distinct dominant electrophysiological features for four unique IN subtypes in L1 that were previously identified from mice of either gender: Canopy cells
show an irregular firing pattern near rheobase; Neurogliaform cells (NGFCs) are
late-spiking, and their firing rate accelerates during current injections; cells with strong
expression of the a7 nicotinic receptor (a7 cells), display onset (rebound) bursting;
vasoactive intestinal peptide (VIP) expressing cells exhibit high input resistance, strong
adaptation, and irregular firing. Computational modeling revealed that these diverse
neurophysiological features could be explained by an extended
exponential-integrate-and-fire neuron model with varying contributions of a slowly
inactivating K+ channel (SIK), a T-type Ca2+ channel, and a spike-triggered
Ca2+-dependent K+ channel. In particular, we show that irregular firing results from
square-wave bursting through a fast-slow analysis. Furthermore, we demonstrate that
irregular firing is frequently observed in VIP cells due to the interaction between strong
adaptation and a SIK channel. At last, we reveal that the VIP and a7 cell models
resonant with Alpha/Theta band input through a dynamic gain analysis. |
| 24. |
Syn Plasticity Regulation + Information Processing in Neuron-Astrocyte Networks (Vuillaume et al 21)
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"... we consider a model of astrocyte-regulated synapses to investigate this hypothesis at the level of layered networks of interacting neurons and astrocytes. Our simulations hint that gliotransmission sustains the transfer function across layers, although it decorrelates the neuronal activity from the signal pattern..." |
| 25. |
Vibration-sensitive Honeybee interneurons (Ai et al 2017)
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"Female honeybees use the “waggle dance” to communicate the location of nectar sources to their hive mates. Distance information is encoded in the duration of the waggle phase (von Frisch, 1967). During the waggle phase, the dancer produces trains of vibration pulses, which are detected by the follower bees via Johnston's organ located on the antennae. To uncover the neural mechanisms underlying the encoding of distance information in the waggle dance follower, we investigated morphology, physiology, and immunohistochemistry of interneurons arborizing in the primary auditory center of the honeybee (Apis mellifera). We identified major interneuron types, named DL-Int-1, DL-Int-2, and bilateral DL-dSEG-LP, that responded with different spiking patterns to vibration pulses applied to the antennae. Experimental and computational analyses suggest that inhibitory connection plays a role in encoding and processing the duration of vibration pulse trains in the primary auditory center of the honeybee." |
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