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_instRate( spikes, length, spikes_memory, NrnPerFeat ):
    #Instantaneous firing rate
    
    spk = np.zeros(int(np.size(spikes,axis=0)/NrnPerFeat))
    for nn in np.arange(np.size(spk,axis=0)):
        spk[nn] = np.sum(spikes[nn*NrnPerFeat:(nn+1)*NrnPerFeat],axis=0)
   
    
    spikes_memory[:,0:-1] = spikes_memory[:,1:]
    spikes_memory[:,-1] = spk
    
    inst_rate = np.sum(spikes_memory,axis=1)/(0.001*length*NrnPerFeat)

    return inst_rate,spikes_memory

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