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.
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;
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 numpy as np
import FSIN as sim
import os
import brian2 as b2
import sys
import matplotlib.pyplot as plt
TigerFish = True
if TigerFish: 	# For Tigerfish use only
	cache_dir = os.path.expanduser('~/.cython/brian-pid-{}'.format(os.getpid()))
	b2.prefs.codegen.runtime.cython.cache_dir = cache_dir
	b2.prefs.codegen.runtime.cython.multiprocess_safe = False

method = "rk4"; # methods "rk4" and "gsl_rk8pd" give similar results
dt = 1.e-3;

Ess = [-75.];#-55.

ggaps= 2.


gm_scale = factor;




Nnets = 30;
Ncycs = 30;

# Total simulated time. Theta frequency is 8 Hz, i.e. period of 125 ms.
# sim_time given as multiple of period, i.e. number of simulated cycles.
# Only the last two are plotted. To remove effects of transients.
sim_time = (4.+Ncycs)*250.; 

# We apply a downsampling to the computed smoothed population rates.
# We save values at every dt_dt ms, i.e. the sampling rate is 1.0/dt_ds kHz.
dt_ds = .1;
kds = int(np.round(dt_ds/dt));

for Es in Ess:
    conn_mat = int(sys.argv[1]) 
   #for conn_mat in range(Nnets):
    rates = sim.gewnet(FactorScaleKV3=FactorScaleKV3,gLmin=gLmin,tau_fall=tau_fall,factorTau=factorTau,read_gms=False,scale_gm=True,gm_scale = gm_scale, ggaps=ggaps,gsin=gsin,fsin=fsin,sim_time=sim_time,mod_gL=mod_gL,read_ggaps=read_ggaps,gjs=gjs,method=method,dt=dt,connectivity_matrix=conn_mat,Es=Es)[0][1];
    rates_ds = rates[::kds];
    tmp = np.savetxt("RatesFig7/Esm%d_cm%d_%dHzgChR%d_Fact%d.dat" % (int(np.round(-Es)),conn_mat,int(fsin),int(gsin), int(factor)), rates_ds );