Circuits that contain the Modeling Application : Brian (Home Page)

(Brian is a new simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists. Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages.)
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    Models   Description
1. An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al., 2013)
A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.
2. Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)
"... Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. ... Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. ..."
3. CA1 network model for place cell dynamics (Turi et al 2019)
Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons.
4. CA1 pyramidal neuron network model (Ferguson et al 2015)
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.
5. Computing with neural synchrony (Brette 2012)
"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ..."
6. Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)
"Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. ..."
7. CRH modulates excitatory transmission and network physiology in hippocampus (Gunn et al. 2017)
This model simulates the effects of CRH on sharp waves in a rat CA1/CA3 model. It uses the frequency of the sharp waves as an output of the network.
8. Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017)
Here we investigate the role of dentate granule cell dendrites in pattern separation. The model consists of point neurons (Integrate and fire) and in principal neurons, the granule cells, we have incorporated various number of dendrites.
9. Diffusive homeostasis in a spiking network model (Sweeney et al. 2015)
In this paper we propose a new mechanism, diffusive homeostasis, in which neural excitability is modulated by nitric oxide, a gas which can flow freely across cell membranes. Our model simulates the activity-dependent synthesis and diffusion of nitric oxide in a recurrent network model of integrate-and-fire neurons. The concentration of nitric oxide is then used as homeostatic readout which modulates the firing threshold of each neuron.
10. Effect of polysynaptic facilitaiton between piriform-hippocampal network stages (Trieu et al 2015)
This is a model of a multistage network with stages representing regions and synaptic contacts from the olfactory cortex to region CA1 of the hippocampus in Brian2 spiking neural network simulator (Trieu et al 2015). It is primarily designed to assess how synaptic facilitation at multiple stages in response to theta firing changes the output of the network. Further developments will be posted at: github.com/cdcox/multistage_network This model was prepared by Conor D Cox, University of California, Irvine For questions please contact Conor at cdcox1@gmail.com
11. Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocmé et al., 2015)
This model, a simplified olfactory bulb network with mitral and granule cells, proposes a framework for two regimes of oscillation in the olfactory bulb: 1 - a weak inhibition regime (with no granule spike) where the network oscillates in the gamma (40-90Hz) band 2 - a strong inhibition regime (with granule spikes) where the network oscillates in the beta (15-30Hz) band. Slow modulations of sensory and centrifugal inputs, phase shifted by a quarter of cycle, possibly combined with short term depression of the mitral to granule AMPA synapse, allows the network to alternate between the two regimes as observed in anesthetized animals.
12. Hierarchical network model of perceptual decision making (Wimmer et al 2015)
Neuronal variability in sensory cortex predicts perceptual decisions. To investigate the interaction of bottom-up and top-down mechanisms during the decision process, we developed a hierarchical network model. The network consists of two circuits composed of leaky integrate-and-fire neurons: an integration circuit (e.g. LIP, FEF) and a sensory circuit (MT), recurrently coupled via bottom-up feedforward connections and top-down feedback connections. The integration circuit accumulates sensory evidence and produces a binary categorization due to winner-take-all competition between two decision-encoding populations (X.J. Wang, Neuron, 2002). The sensory circuit is a balanced randomly connected EI-network, that contains neural populations selective to opposite directions of motion. We have used this model to simulate a standard two-alternative forced-choice motion discrimination task.
13. Input strength and time-varying oscillation peak frequency (Cohen MX 2014)
The purpose of this paper is to argue that a single neural functional principle—temporal fluctuations in oscillation peak frequency (“frequency sliding”)—can be used as a common analysis approach to bridge multiple scales within neuroscience. The code provided here recreates the network models used to demonstrate changes in peak oscillation frequency as a function of static and time-varying input strength, and also shows how correlated frequency sliding can be used to identify functional connectivity between two networks.
14. Modeling epileptic seizure induced by depolarization block (Kim & Dykamp 2017)
"The inhibitory restraint necessary to suppress aberrant activity can fail when inhibitory neurons cease to generate action potentials as they enter depolarization block. We investigate possible bifurcation structures that arise at the onset of seizure-like activity resulting from depolarization block in inhibitory neurons. Networks of conductance based excitatory and inhibitory neurons are simulated to characterize different types of transitions to the seizure state, and a mean field model is developed to verify the generality of the observed phenomena of excitatory-inhibitory dynamics. ..."
15. Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
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.
16. Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper (Brette et al. 2007). The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005).
17. Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)
The model code as described in "A neural mass model based on single cell dynamics to model pathophysiology, Zandt et al. 2014, Journal of Computational Neuroscience" A Neural mass model (NMM) derived from single cell dynamics in a bottom up approach. Mean and standard deviation of the firing rates in the populations are calculated. The sigmoid is derived from the single cell FI-curve, allowing for easy implementation of pathological conditions. NMM is compared with a detailed spiking network model consisting of HH neurons. NMM code in Matlab. The network model is simulated using Norns (ModelDB # 154739)
18. Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.
19. Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)
" ... In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. ..."
20. Spontaneous weakly correlated excitation and inhibition (Tan et al. 2013)
Brian code for Tan et al. 2013.
21. STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).

Re-display model names without descriptions