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View Code? Open in Web Editor NEWMesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018) (PyNN)
Home Page: https://modeldb.science/264597
Mesoscopic dynamics from AdEx recurrent networks (Zerlaut et al JCNS 2018) (PyNN)
Home Page: https://modeldb.science/264597
Pynn demo files to simulate networks of RS-FS cells --------------------------------------------------- Those PyNN demo files simulate a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons. We study here the network at the level of single cells, then his spontaneous activity and finally its response to time-varying external input, as described in the following paper: Zerlaut Y, Chemla S, Chavane F, Destexhe A (2018) Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons. J Comput Neurosci 44:45-61 These files were contributed By Amelie Soler (Destexhe lab). Many example output images are provided in the subfolders. This paper presents a RS-FS mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. It uses 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. It compares 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. Second, it investigates the response of the network to time-varying external input. Finally, to model VSDi signals, a one-dimensional ring model made of interconnected RS-FS mean-field units is used. The simulations shown here are reproductions of Figure 2 (simulation 1), Figure 3-a,b (simulation 2) and Figure 5-a,b (simulation 3) of the paper. SImulation 1: It shows the response of the single cell models (RS in green and FS in red) to an external current step of 200 pA lasting 300 ms. The parameters of the cells are the same as the ones presented on Table 1 in the article. The membrane potentials are plotted with the spikes (represented with dots) on the same graph. Simulation 2: This script shows the spontaneous activity of the network. The network is made of 8000 excitatory neurons and 2000 inhibitory neurons. Those two populations of neurons are randomly connected (internally and mutually) with a connectivity probability of 5%. Plus a feedforward input (a ramp of 4HZ) on the inhibitory population and on the excitatory population is added. We simulate the network for 1000ms. The spiking activity and firing rate of the network is plotted (green: excitatory neurons, red: inhibitory neurons). Simulation 3: This last script shows the response of the network to time-varying external input. The duration of simulation and the construction of the network is the same. Only here, the feedforward input on the excitatory population is varying between 4Hz and 6H (following theoretically a gaussian in the paper). During 100ms, it’s the first part of the gaussian; here we simulate the gaussian used by a ramp. Then for 200ms, it’s the second part of the gaussian, we simulate it with a descending ramp. Identically, the spiking activity and firing rate of the network is plotted (green: excitatory neurons, red: inhibitory neurons). If you use this for your research, please cite the above paper. Amélie Soler CNRS, Gif sur Yvette, France http://cns.iaf.cnrs-gif.fr
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