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];
from __future__ import division
import numpy as np


def  f_corr_assoc_var_60(NrON,NrIn_perFeat,NrFeat,prob_var,inp_time,timeStep,buffertime,StimFreq):
  
    

    nr_nrn = NrFeat*NrIn_perFeat
    itime = int(inp_time/timeStep) # nr of timesteps for active input
    btime = int(buffertime/timeStep) # nr of timesteps between two active inputs
    
    prob_v = np.random.rand(1)
    
    if prob_v<prob_var[0]:
        rvar = np.expand_dims(np.array([1,1,1,1,0,0]),1)
        rvar = np.kron(StimFreq*(rvar),np.ones((NrIn_perFeat,1)))
        r_matrix = np.append(np.tile(rvar,[1,itime]), np.zeros((nr_nrn,btime)),axis=1)     
        ftr=1
    elif prob_v<prob_var[0]+prob_var[1]:
        rvar = np.expand_dims(np.array([0,0,1,1,1,1]),1)
        rvar = np.kron(StimFreq*(rvar),np.ones((NrIn_perFeat,1)))
        r_matrix = np.append(np.tile(rvar,[1,itime]), np.zeros((nr_nrn,btime)),axis=1)  
        ftr=2
    else:
        rvar = np.expand_dims(np.array([0,0,0,0,0,0]),1)
        rvar = np.kron(StimFreq*(rvar),np.ones((NrIn_perFeat,1)))
        r_matrix = np.append(np.tile(rvar,[1,itime]), np.zeros((nr_nrn,btime)),axis=1) 
        ftr=3
    
       
    r_matrix = np.append(r_matrix , np.zeros((NrON,np.size(r_matrix,axis=1))), axis=0)
    
    return r_matrix,ftr
    

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