Inhibition of bAPs and Ca2+ spikes in a multi-compartment pyramidal neuron model (Wilmes et al 2016)

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Accession:187603
"Synaptic plasticity is thought to induce memory traces in the brain that are the foundation of learning. To ensure the stability of these traces in the presence of further learning, however, a regulation of plasticity appears beneficial. Here, we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials (bAPs) and calcium spikes, i.e., by gating the coincidence signals required for Hebbian forms of plasticity. We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials (EPSPs). In a computational analysis of conductance-based multi-compartmental models, we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner, enabling a binary switch of coincidence signals and plasticity. ..."
Reference:
1 . Wilmes KA, Sprekeler H, Schreiber S (2016) Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons. PLoS Comput Biol 12:e1004768 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Neocortex; Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Neocortex V1 L6 pyramidal corticothalamic GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Dendritic Action Potentials; Synaptic Plasticity; Synaptic Integration;
Implementer(s): Wilmes, Katharina A. [katharina.wilmes at googlemail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Neocortex V1 L6 pyramidal corticothalamic GLU cell;
#!/usr/bin/env python

"""script for producing data for Figures 3B and 4B,
timing and strength of inhibition is varied,
inhibiton can be placed on the proximal or distal
apical dendrite """

# _title_     : timing.py
# _author_     : Katharina Anna Wilmes
# _mail_     : katharina.anna.wilmes __at__ cms.hu-berlin.de


# --Imports--
import sys
import os
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from time import time
import neuron
from neuron import h

from neuronmodel import *
from config_timing import *
from sim import *

#set path for saving data
path = './'


NO_REPS = 1
DT=0.1 # ms, set the integration step
POST_AMP = 0.3 # nA, amplitude of current injection to trigger AP/bAP
WARM_UP=1000 # ms
identifier = '2015-01-13-00h00m00s'
savepath = '%s%s'%(path,identifier)
if not os.path.exists(savepath):
	os.mkdir(savepath)
 
def _get_current_trace(freq,delta_t,ap_delay, t_stop,pre=False,test=True) :
    trace = np.zeros(t_stop/DT)
    for i in range(NO_REPS) :
        if(pre) :
            start_t = (0 + i* (1000.0/freq) + WARM_UP)
        else :
            start_t = (0 + delta_t - ap_delay + i* (1000.0/freq) + WARM_UP)
        end_t = (start_t+2)
        if(test) :
            print 'start_t=%g, end_t=%g (t_stop=%g, len(trace)=%f)' % (start_t,end_t,t_stop,len(trace))
        trace[start_t/DT:end_t/DT] = POST_AMP
    return trace
    
def forceAspect(axis,aspect=1):
    im = axis.get_images()
    extent =  im[0].get_extent()
    axis.set_aspect(abs((extent[1]-extent[0])/(extent[3]-extent[2]))/aspect)

def define_color():

    startcolor = '#FDCC8A' 
    midcolor = '#FC8D59'
    endcolor = '#D7301F'

    cmap2 = mpl.colors.LinearSegmentedColormap.from_list('own',[startcolor,midcolor,endcolor])
    plt.cm.register_cmap(cmap=cmap2)
    cmap2.set_under('#000000')
       

def plot_amp_weight(matrix,AP, x, savepath,name):

    diff = matrix[:,:]/matrix[0,:] # normalize to values with 0 shunt_weight
    noAP_idx = AP[:,:]<80
    diff[noAP_idx] = -2

    define_color()
    fig = plt.figure()
    ax = plt.subplot(111)    
    imgplot = plt.imshow(diff[:,:],vmin=-0,vmax=1,origin = 'lower',aspect = 'equal',interpolation="nearest")
    imgplot.set_cmap('own')

    plt.xticks(np.arange(0,100,10),np.arange(-5,5,1))
    plt.xlabel('delay [ms]')
    plt.yticks(np.arange(0,10,2),np.arange(0,0.1,0.02))
    plt.ylabel('weight [nS]')

    forceAspect(ax)
    cbar_ax = fig.add_axes([0.8, 0.1, 0.03, 0.8])
    cb = fig.colorbar(imgplot, cax=cbar_ax)
    plt.savefig('%s/overview%s.eps'%(savepath,name))

def main():

    my_rawdata = {}


    condition = params['condition']    
    
    #inputs
    freq = params['Input']['freq'].value

    # Synapses
    pos = params['Synapse']['pos'].value
    wee = params['Synapse']['distal_weight'].value

    shunt_pos = params['shunt']['shunt_pos'].value
    proximal_shunt_pos = params['shunt']['proximal_shunt_pos'].value
    distal_shunt_pos = params['shunt']['distal_shunt_pos'].value
    basal_shunt_pos = params['shunt']['basal_shunt_pos'].value
    proximal_shunt_compartment = params['shunt']['proximal_shunt_compartment']
    distal_shunt_compartment = params['shunt']['distal_shunt_compartment']
    basal_shunt_compartment = params['shunt']['basal_shunt_compartment']
    AP_DELAY = params['Synapse']['AP_DELAY'].value

    delta_t = params['STDP']['delta_t'].value
    
    sim_params = params['sim']
    shunt_params = params['shunt']    
    
    delay_start = params['shunt']['delay_start'].value
    delay_end = params['shunt']['delay_end'].value
    
    shunt_weight_range = np.arange(0.0,0.1,0.01)
    shunt_delay_range = np.arange(delay_start,delay_end,0.1)

    shunt_weight_range = np.arange(0.0,0.1,0.05)
    shunt_delay_range = np.arange(-1,1,1)

    source = False
    
    AP = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))

    distal_bAP = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))
    distal_Ca = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))
    distal_Ca_max = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))

    oblique_bAP = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))
    basal_bAP = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))
    AP_time = np.zeros((len(shunt_weight_range),len(shunt_delay_range)))

    count = 1
    total_iterations = len(shunt_weight_range)*len(shunt_delay_range)
    
    for i, shunt_weight in enumerate(shunt_weight_range):
        for j, shunt_delay in enumerate(shunt_delay_range): 
            print "iteration %d from %d"%(count,total_iterations) 
        
            cell = Neuron()
            sim = Simulation(cell,sim_params)
            sim.dt = DT
            sim.v_init = -70
            total_time = WARM_UP+NO_REPS*(1000.0/freq)+100
            interval = 1000.0/freq
        
        
            # somatic current injection
            ic = h.IClamp(cell.soma(0.5))
            ic.delay = 0
            ic.dur=1e9
            current_trace = _get_current_trace(freq,delta_t,AP_DELAY,total_time,pre=False)
            current_vec = h.Vector(current_trace)
            current_vec.play(ic._ref_amp,DT)
        
            if pos == 3:
                # distal excitation
                syn = h.Exp2Syn(cell.branches[0](0.1))
                weight = wee
                syn.e = 0
                syn.tau1 = 0.5
                syn.tau2 = 2
                exstim = h.NetStim()
                exstim.number = NO_REPS
                exstim.interval = interval
                exstim.start = WARM_UP # reverse: +DELTA_T
                exstim.noise= 0
                nc = h.NetCon(exstim,syn,0,0,weight)
        
            sim.sim_time = total_time
        
            inh_delay = WARM_UP + delta_t - AP_DELAY + shunt_delay
        
            # inhibition
            if shunt_pos == 0:
                print "no inhibition"
            elif shunt_pos == 1: # distal
                sim.set_Shunt(shunt_params, distal_shunt_pos, shunt_weight, distal_shunt_compartment,source,inh_delay,NO_REPS, interval)
            elif shunt_pos == 2: # proximal
                sim.set_Shunt(shunt_params, proximal_shunt_pos, shunt_weight, proximal_shunt_compartment,source,inh_delay,NO_REPS,interval)
            elif shunt_pos == 3: # basal
                sim.set_Shunt(shunt_params, basal_shunt_pos, shunt_weight,basal_shunt_compartment,source,inh_delay,NO_REPS,interval)                
            else:
                raise ValueError
        
            # recording
            trec = h.Vector()
            trec.record(h._ref_t)
            vrec = h.Vector()
            vrec.record(cell.soma(0.5)._ref_v)
            vdrec = h.Vector()
            vdrec.record(cell.branches[0](0.5)._ref_v)
            vorec = h.Vector()
            vorec.record(cell.oblique_branch(0.9)._ref_v)
            vbrec = h.Vector()
            vbrec.record(cell.basal_main(0.9)._ref_v)
            cadrec = h.Vector()
            cadrec.record(cell.branches[0](0.5)._ref_ica)
        
            if not shunt_pos == 0:
                sim.set_synaptic_recording(switch=False,all=False)
        
            sim.go()

            t = np.array(trec)
            v = np.array(vrec)
            vd = np.array(vdrec)
            vo = np.array(vorec)
            vb = np.array(vbrec)
            cad = np.array(cadrec)
            currentv =  np.array(current_vec)
            
            AP[i,j] = np.max(v)-(-75)
            distal_bAP[i,j] = np.max(vd)-(-75)
            distal_Ca[i,j] = np.sum(cad)
            distal_Ca_max[i,j] = np.max(np.abs(cad))
            oblique_bAP[i,j] = np.max(vo)-(-75)
            basal_bAP[i,j] = np.max(vb)-(-75)
            AP_time[i,j] = t[np.argmax(v[:1200])]
            
            count += 1


    # plot
    if condition == 'bAP':
        plot_amp_weight(oblique_bAP, AP, shunt_weight_range, savepath, 'oblique_bAP')
    elif condition == 'ca':
        plot_amp_weight(distal_Ca, AP, shunt_weight_range, savepath, 'distal_Ca')
        plot_amp_weight(distal_Ca_max, AP, shunt_weight_range, path, 'distal_Ca_max')
    else:
        print 'condition undefined'
    
    # data
    my_rawdata['t'] = t
    my_rawdata['v'] = v
    my_rawdata['vd'] = vd
    my_rawdata['vo'] = vo
    my_rawdata['vb'] = vb
    my_rawdata['cad'] = cad

    my_rawdata['currentv'] = currentv
    rawdata = {'raw_data': my_rawdata}


    return rawdata


if __name__ == '__main__':

    rawdata = main()