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 data of Figure 1F
stimulates neuron somatically to fire at different frequencies"""

# _title_     : critical_frequency.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_validation import *
from sim import *



path = './'

NO_REPS = 5 # 5 somatic stimulations
RESET = True
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
DELTA_T=10 #ms, does not matter in this case, only somatic stimulation
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 + 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 main():

    my_rawdata = {}

    sim_params = params['sim']
    #inputs
    frequencies = np.arange(10,100,10)

    distal_integral = np.zeros((len(frequencies)))

    for i, freq in enumerate(frequencies):
        cell = Neuron()
        sim = Simulation(cell,sim_params)
        sim.dt = DT
    
    
        # somatic stimulation
        ic = h.IClamp(cell.soma(0.5))
        ic.delay = 0
        ic.dur=1e9
        total_time = WARM_UP+NO_REPS*(1000.0/10)+100
        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)
    
        sim.sim_time = total_time
    
        # recording
        trec = h.Vector()
        trec.record(h._ref_t)
    
        sim.set_highestres_recording()    
        sim.go()
        t = np.array(trec)
        recording = sim.get_highestres_recording()
        num_spikes, rate = sim.get_rate(0,total_time)
        
        distal_voltage = recording[20,:]
        index = int(1000/0.1) # take integral from 1000 ms to end of simulation (600ms interval)
        distal_integral[i] = np.sum(distal_voltage[index:]-(-75))
    
    fig = plt.figure()
    plt.plot(frequencies,distal_integral/distal_integral[-1],'k',marker='o',mfc="w",mew=1)
    plt.xlabel("frequency [Hz]", fontsize = 'large')
    plt.ylabel("Integral of distal voltage", fontsize = 'large')
    plt.axis(xmin = 0,xmax=90)
    plt.axis(ymin = 0.2,ymax=1.1)
    plt.savefig('%s/crit_freq.eps'%(savepath))    
    

    my_rawdata['t'] = t
    my_rawdata['num_spikes'] = num_spikes
    my_rawdata['rate'] = rate
    my_rawdata['recording'] = recording

    rawdata = {'raw_data': my_rawdata}

    return rawdata



if __name__ == '__main__':

    rawdata = main()