CA1 network model for place cell dynamics (Turi et al 2019)

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Accession:246546
Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons.
Reference:
1 . Turi GF, Li W, Chavlis S, Pandi I, O’Hare J, Priestley JB, Grosmark AD, Liao Z, Ladow M, Zhang JF, Zemelman BV, Poirazi P, Losonczy A (2019) Vasoactive Intestinal Polypeptide-Expressing Interneurons in the Hippocampus Support Goal-Oriented Spatial Learning Neuron
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus; Mouse;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell; Hippocampus CA1 basket cell - CCK/VIP; Hippocampus CA1 bistratified cell; Hippocampus CA1 axo-axonic cell; Hippocampus CA1 stratum oriens lacunosum-moleculare interneuron ; Hippocampal CA1 CR/VIP cell;
Channel(s): I A; I h; I K,Ca; I Calcium; I Na, leak; I K,leak; I M;
Gap Junctions:
Receptor(s): GabaA; GabaB; NMDA; AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Brian;
Model Concept(s): Place cell/field;
Implementer(s): Chavlis, Spyridon [schavlis at imbb.forth.gr]; Pandi, Ioanna ;
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; GabaA; GabaB; AMPA; NMDA; I A; I K,leak; I M; I h; I K,Ca; I Calcium; I Na, leak;
/
Turi_et_al_2018
background_noise
poisson_input.py
                            
import brian
import numpy as np
import os, sys

nruns = int(sys.argv[1])

for nrun in xrange(1, nruns+1):
    brian.seed(nrun)
    print 'RUN: ' + str(nrun)
    brian.reinit(states = True)
    brian.clear(erase   = True, all = True)
    rate = int(sys.argv[2])
    foldername = 'rate'+str(rate)+'/run_'+str(nrun)
    os.system('mkdir -p -v '+foldername)
    
    N  = 1000
    time_input = 23000 * brian.ms
    P  = brian.PoissonGroup(N)
    S = brian.SpikeMonitor(P)
    
    P.rate = rate * brian.Hz
    brian.run(time_input, report='text', report_period = 10 * brian.second)
    
    fname = 'noise_'    
    for s in xrange(len(S.spiketimes)):
        spiketimes = [round(1000*x,1)+50 for x in list(S.spiketimes[s])]
        np.savetxt(foldername+'/'+fname+str(s)+'.txt',spiketimes,fmt='%10.1f',newline='\n')
    
    


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