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]
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

"""produces voltage and current traces for Figure 1B-E"""

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


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

from neuronmodel import *
from config_model_stim import *
from sim import *


path = './'

SCEN = 3
NO_REPS = 1
RESET = True
DT=0.1 # ms, set the integration step
POST_AMP = 0.3# nA, amplitude of current injection to trigger the AP/bAP
WARM_UP=1000 # ms
AP_DELAY = 0 #ms, AP needs 4ms after stimulation to become initiated
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,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 create_syn(syn_type,pos, thresh):
    syn = h.ExpSynSTDP(pos)
    syn.thresh = thresh
    return syn


def main():

    my_rawdata = {}

    #inputs
    freq = params['Input']['freq'].value
    wee = params['Input']['wee'].value
    wee_strong = params['Input']['wee_strong'].value

    # Synapses
    scen = SCEN
    delta_t = params['STDP']['delta_t'].value
    
    sim_params = params['sim']

    source = False

    cell = Neuron()
    sim = Simulation(cell,sim_params)
    sim.dt = DT
    sim.v_init = -70
    total_time = WARM_UP+NO_REPS*(1000.0/freq)+100

    if (scen == 1) or (scen == 3):
        # trigger AP/bAP
        ic = h.IClamp(cell.soma(0.5))
        ic.delay = 0
        ic.dur=1e9
        current_trace = _get_current_trace(freq,delta_t,total_time,pre=False)
        current_vec = h.Vector(current_trace)
        current_vec.play(ic._ref_amp,DT)

    if (scen == 2) or (scen == 3) or (scen==4):
        # distal excitation
        syn = h.Exp2Syn(cell.branches[0](0.1))
        if scen == 4:
            # strong distal excitation
            weight = wee_strong
        else:
            weight = wee
        syn.e = 0
        syn.tau1 = 0.5
        syn.tau2 = 2
        interval = 1000.0/freq
        exstim = h.NetStim()
        exstim.number = NO_REPS
        exstim.interval = interval
        exstim.start = WARM_UP
        exstim.noise= 0
        nc = h.NetCon(exstim,syn,0,0,weight)

    sim.sim_time = total_time

    # 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)
    vbrec = h.Vector()
    vbrec.record(cell.basal_main(0.5)._ref_v)
    vorec = h.Vector()
    vorec.record(cell.oblique_branch(0.9)._ref_v)
    if (scen == 2) or (scen == 4):
        currentrec = h.Vector()
        currentrec.record(syn._ref_i)

    # record state vars
    vit2m, vit2h, vscam, vscah, vkcan, vna3dendm, vna3dendh = [h.Vector() for x in xrange(7)]
    vit2m.record(cell.branches[0](0.5).it2._ref_m)
    vit2h.record(cell.branches[0](0.5).it2._ref_h)
    vscam.record(cell.branches[0](0.5).sca._ref_m)
    vscah.record(cell.branches[0](0.5).sca._ref_h)
    vkcan.record(cell.branches[0](0.5).kca._ref_n)
    vna3dendm.record(cell.branches[0](0.5).na3dend._ref_m)
    vna3dendh.record(cell.branches[0](0.5).na3dend._ref_h)

    # run simulation
    sim.go()

    t = np.array(trec)
    v = np.array(vrec)
    vd = np.array(vdrec)
    vb = np.array(vbrec)
    vo = np.array(vorec)

    it2m = np.array(vit2m)
    it2h = np.array(vit2h)
    scam = np.array(vscam)
    scah = np.array(vscah)
    kcan = np.array(vkcan)
    na3dendm = np.array(vna3dendm)
    na3dendh = np.array(vna3dendh)

    # plot traces
    if scen == 1:
        step = np.array(current_vec)
        x = np.arange(len(step))
        plt.figure()
        plt.plot(x*DT,step,'k',label='vd')
        plt.xlim((990,1100))
        plt.ylim((0,3.6))
        plt.xlabel("Time [ms]", fontsize = 'large')
        plt.ylabel("Current [nA]", fontsize = 'large')
        plt.savefig('%s/step.eps'%(savepath))

    if (scen == 2) or (scen == 4):
        crec = np.array(currentrec)
        x = np.arange(len(crec))
        plt.figure()
        plt.plot(x*DT,abs(crec),'r',label='vd')
        plt.xlim((990,1100))
        plt.ylim((0,3.6))
        plt.xlabel("Time [ms]", fontsize = 'large')
        plt.ylabel("Current [nA]", fontsize = 'large')
        plt.savefig('%s/crec%d.eps'%(savepath,scen))
    plt.figure()
    plt.plot(t,v,'k',label='v')
    plt.hold(True)
    plt.plot(t,vd,'r',label='vd')
    plt.xlabel("Time [ms]", fontsize = 'large')
    plt.xlim((990,1100))
    plt.ylim((-80,40))
    plt.ylabel("Voltage [mV]", fontsize = 'large')
    plt.savefig('%s/vrec%d.eps'%(savepath,scen))
    
    plt.figure()
    plt.plot(t,it2m,'k-',label='v')
    plt.hold(True)
    plt.plot(t,it2h,'k--',label='v')
    plt.plot(t,scam,'r-',label='v')
    plt.plot(t,scah,'r--',label='v')
    plt.plot(t,kcan,'g-',label='vd')
    plt.plot(t,na3dendm,'m-',label='v')
    plt.plot(t,na3dendh,'m--',label='v')

    plt.xlabel("Time [ms]", fontsize = 'large')
    plt.xlim((990,1100))
    #plt.ylim((-80,40))
    plt.ylabel("state", fontsize = 'large')
    plt.savefig('%s/statevars%d.eps'%(savepath,scen))

    # delete
    del(cell)
    del(sim)

    del(trec);del(vrec)

    my_rawdata['t'] = t
    my_rawdata['v'] = v
    my_rawdata['vd'] = vd
    my_rawdata['vb'] = vb
    my_rawdata['vo'] = vo
    my_rawdata['it2m'] = it2m
    my_rawdata['it2h'] = it2h
    my_rawdata['scam'] = scam
    my_rawdata['scah'] = scah
    my_rawdata['kcan'] = kcan
    my_rawdata['na3dendm'] = na3dendm
    my_rawdata['na3dendh'] = na3dendh


    rawdata = {'raw_data': my_rawdata}

    return rawdata



if __name__ == '__main__':

    rawdata = main()

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