Modeling dendritic spikes and plasticity (Bono and Clopath 2017)

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Biophysical model and reduced neuron model with voltage-dependent plasticity.
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):
Gap Junctions:
Simulation Environment: Brian 2; Python;
Model Concept(s): Synaptic Plasticity; STDP; Dendritic Action Potentials;
Implementer(s): Bono, Jacopo [ j.bono13 at];
from __future__ import division
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt

def  f_plotWeights_subplots( weightData, NrON,maxW):
    #plotWeights: plot the synaptic weights i.f.o. time
    #   Detailed explanation goes here
        # set fonts
        titleFont = 34
        normalFont = 34
        f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
        location1p2d = 1
        proxDist = (location1p2d-1)*NrON
        weightData2 = weightData[proxDist:NrON+proxDist, :]
        for diagVar in range(NrON):
            weightData2[diagVar,diagVar] = -0.1
        pt1 = ax1.pcolor(weightData2, cmap='Greys', vmin=0, vmax=maxW)
        location1p2d = 2
        proxDist = (location1p2d-1)*NrON
        weightData2 = weightData[proxDist:NrON+proxDist, :]
        for diagVar in range (NrON):
            weightData2[diagVar,diagVar] = -0.1
        pt2 = ax2.pcolor(weightData2, cmap='Greys', vmin=0, vmax=maxW)
        cbar1 = plt.colorbar(pt2)
#        cbar2 = plt.colorbar(pt1)
#        cbar2.set_clim(0,1)


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