Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocmé et al., 2015)

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Accession:185014
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.
Reference:
1 . David F, Courtiol E, Buonviso N, Fourcaud-Trocmé N (2015) Competing Mechanisms of Gamma and Beta Oscillations in the Olfactory Bulb Based on Multimodal Inhibition of Mitral Cells Over a Respiratory Cycle. eNeuro [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell;
Channel(s): I_Ks;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: Brian; Python;
Model Concept(s): Short-term Synaptic Plasticity; Gamma oscillations; Beta oscillations; Olfaction;
Implementer(s):
Search NeuronDB for information about:  Olfactory bulb main mitral cell; Olfactory bulb main interneuron granule MC cell; GabaA; AMPA; I_Ks;
# -*- coding: utf-8 -*-
"""
Simple quadratic integrate-and-fire (QIF) model for the granule cells.
Parameters were fitted to match model f-I curve with granule f-I curve of Davison (2001, PhD Thesis)
"""

from brian import *

print "Granule equation initialization"

# Granule membrane parameters
V_T=-60.*mV
I_T=0.02*nA # threshold of the Davison f-I curve

# All next 3 variables are linked to reproduce the Davison f-I curve (# example with a multiplicative factor)
Delta_T=0.1*mV # Delta_T*factor
tau_m=60*ms # tau_m/factor
gL_G=16.666*nS # gL_G*factor => not a real leak constant (because such a constant has no meaning in a QIF model)


# Synaptic current parameters
tauE=3.*ms
Ee = 0.*mV
gE_max=4.*nS # gE_max*factor <-- if excitation must remain the same when a factor is applied above
 
QIF_eqs="""
dV/dt =  ((1/(2*Delta_T))*(V-V_T)**2 - I_T/gL_G + Iinj/gL_G - (sE*gE_max + gEinj)/gL_G*(V-Ee))/tau_m  : volt
Iinj : amp
gEinj : siemens
dsE/dt = -sE/tauE : 1
"""

def QIF_reset(P, spikes):
    P.V[spikes]=-70.*mV # -70*mV in UB4.c