Modeling dendritic spikes and plasticity (Bono and Clopath 2017)

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Accession:232914
Biophysical model and reduced neuron model with voltage-dependent plasticity.
Reference:
1 . Bono J, Clopath C (2017) Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level. Nat Commun 8:706 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Brian 2; Python;
Model Concept(s): Synaptic Plasticity; STDP; Dendritic Action Potentials;
Implementer(s): Bono, Jacopo [ j.bono13 at imperial.ac.uk];
import numpy as np

import sys
sys.path.append('../')

from f_create_spiketrain_var2 import f_create_spiketrain_var2
from f_corr_assoc_var_60 import f_corr_assoc_var_60

def f_generateInputs_60(itervr,buffertime,inp_time,rate,NrON,NrIn_perFeat,NrFeatures,timeStep,FeatProbabs):
    ispikes = []
    
    for ww in np.arange(itervr):
#        FeatProbabs = [.7,.15]
        
        s_rates,ftr = f_corr_assoc_var_60(NrON,NrIn_perFeat,NrFeatures,FeatProbabs,inp_time,timeStep,buffertime,rate) 
        spiketrain = f_create_spiketrain_var2(s_rates, timeStep) 
        
        if len(ispikes)>0:
            ispikes = np.append(ispikes,spiketrain,axis=1)  
        else:
            ispikes = spiketrain 
    
    return ispikes,ftr

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