Mean field model for Hodgkin Huxley networks of neurons (Carlu et al 2020)

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Accession:263259
"We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fire model to the more complex Hodgkin-Huxley and Morris-Lecar models. We show that the resulting mean-field models are capable of predicting the correct spontaneous activity of both excitatory and inhibitory neurons in asynchronous irregular regimes, typical of cortical dynamics. Moreover, it is possible to quantitatively predict the population response to external stimuli in the form of external spike trains. This mean-field formalism therefore provides a paradigm to bridge the scale between population dynamics and the microscopic complexity of the individual cells physiology."
Reference:
1 . Carlu M, Chehab O, Dalla Porta L, Depannemaecker D, Héricé C, Jedynak M, Köksal Ersöz E, Muratore P, Souihel S, Capone C, Zerlaut Y, Destexhe A, di Volo M (2020) A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models. J Neurophysiol 123:1042-1051 [PubMed]
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Simulation Environment: Python;
Model Concept(s): Methods;
Implementer(s): di Volo, Matteo [matteo.di-volo at cyu.fr]; 
!/bin/sh

#  Script.sh
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#  Created by Matteo di Volo on 18/03/2018.
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Related article: Carlu, Mallory, et al. "A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models." Journal of Neurophysiology (2020) in press



Go to folder "network simulations"

#FIRST THE NETORK SIMULATION go to folder "network simulations"

python ntwk_sim_demo.py --CONFIG 'HH_RS--HH_FS--CONFIG1' --tstop 5000


#THEN PLOTTING THE RESULTS

python plot_single_sim.py




# SIMULATION OF THE MEAN FIELD, SPONTANEOUS ACTIVITY

python compare_with_mean_field.py --CONFIG 'HH_RS--HH_FS--CONFIG1' #(READ THE GOOD FILE)

# RESPONSE TO AN INPUT

python waveform_input.py  --CONFIG 'HH_RS--HH_FS--CONFIG1' -S #simulation of the network

python waveform_input.py  --CONFIG 'HH_RS--HH_FS--CONFIG1'  #corresponding mean field

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