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MEC PV-positive fast-spiking interneuron network generates theta-nested fast oscillations

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Accession:267338
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.
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Entorhinal cortex; Mouse;
Cell Type(s): Entorhinal cortex fast-spiking interneuron;
Channel(s): I Na,t; I K;
Gap Junctions: Gap junctions;
Receptor(s): GabaA;
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: Brian 2;
Model Concept(s): Brain Rhythms; Excitability; Gamma oscillations; Theta oscillations; Short-term Synaptic Plasticity;
Implementer(s): Via, Guillem; Baravalle, Roman;
Search NeuronDB for information about:  GabaA; I Na,t; I K; Gaba; 
import FSIN as sim
import numpy as np
import matplotlib.pyplot as plt


method = "euler";
dt = 5.e-4;
sim_time=100.;
kneu=43

data_1pert_shu = sim.gewnet(kneu=kneu,sim_time=sim_time,return_state=True,Nrec=100,dt=dt,method=method,act_syns=True,mod_gL=False, gjs=False,gext=7.,sin_cond=False,const_cond=True,tau_rise=.3,homo_neurons=True,gms=1.65,dist_syns=False,read_delays=False,                   
read_gms=False,dt_rec=.1,dmin=.8,dmax=.8,connectivity="FID",return_syns=False,read_syns=False,USEm=1.,std=False, Es=-55.,randics=False,one_perturbed=True);
data_rics_shu_d16ms = sim.gewnet(kneu=kneu,sim_time=sim_time,return_state=True,Nrec=100,dt=dt,method=method,act_syns=True,mod_gL=False, gjs=False,gext=7.,sin_cond=False,const_cond=True,tau_rise=.3,homo_neurons=True,gms=1.65,dist_syns=False,read_delays=False,                   
read_gms=False,dt_rec=.1,dmin=1.6,dmax=1.6,connectivity="FID",return_syns=False,read_syns=False,USEm=1.,std=False, Es=-55.,randics=True,one_perturbed=False);

width = 5.2 # in inches
height = 7.# in inches
tmp = plt.figure(figsize=[width,height]);

tmp1 = plt.subplot(3,1,2); tmp = [ plt.plot(data_1pert_shu[1].spike_trains()[kneu]/sim.ms, kneu*np.ones(len(data_1pert_shu[1].spike_trains()[kneu])), "ok", ms=3.) for kneu in range(20) ]; tmp = plt.xlim(0.,100.);
plt.title("B. $E_{syn}$=-55, $\delta$=0.8 ms, perturbed")
tmp1.spines['top'].set_visible(False)
tmp1.spines['right'].set_visible(False)
tmp1.spines['bottom'].set_visible(False)
tmp1.spines['left'].set_visible(False)
tmp1.set_yticks([])
tmp1.set_yticklabels([])
tmp2 = plt.subplot(3,1,3); tmp = [ plt.plot(data_rics_shu_d16ms[1].spike_trains()[kneu]/sim.ms, kneu*np.ones(len(data_rics_shu_d16ms[1].spike_trains()[kneu])), "ok", ms=3.) for kneu in range(20) ]; tmp = plt.xlim(0.,100.);
plt.title("C. $E_{syn}$=-55, $\delta$=1.6 ms, random")
tmp2.spines['top'].set_visible(False)
tmp2.spines['right'].set_visible(False)
tmp2.spines['bottom'].set_visible(False)
tmp2.spines['left'].set_visible(False)
tmp2.set_yticks([])
tmp2.set_yticklabels([])

plt.tight_layout()
plt.savefig("Figures/Fig5BC.png");
plt.savefig("Figures/Fig5BC.eps"); plt.clf();