#!/usr/bin/env python
"""script for producing data for Figures 3C, 4C,
timing and strength of inhibition is varied,
inhibition can be on the basal, proximal or distal apical
dendrite"""
# _title_ : timing_apicalstim.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 matplotlib as mpl
import numpy as np
import neuron
from neuron import h
from neuronmodel import *
from config_timing_apical import *
from sim import *
#set path for saving data
path = './'
NO_EPSPs = 8
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, wait until steady state
AP_DELAY = 0
identifier = '2015-01-13-00h00m00s' # folder name
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 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
w_ee = params['Input']['wee'].value
# Synapses
pos = params['Synapse']['pos'].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']
source = False
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)
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
# set dendritic location of interest
if pos == 1:
syn = h.ExpSynSTDP(cell.basal_main(0.5))
weight = params['Synapse']['basal_weight'].value
elif pos == 2:
syn = h.ExpSynSTDP(cell.oblique_branch(0.5))
weight = params['Synapse']['oblique_weight'].value
elif pos == 3:
syn = h.ExpSynSTDP(cell.branches[0](0.1))
weight = params['Synapse']['distal_weight'].value
else:
syn = h.ExpSynSTDP(cell.soma(0.5))
weight = 0
interval = 1000.0/freq
exstim = h.NetStim()
exstim.number = NO_REPS
exstim.interval = interval
exstim.start = WARM_UP # reverse: +DELTA_T
exstim.noise= 0
exnc = h.NetCon(exstim,syn,0,0,weight)
total_time = WARM_UP+NO_REPS*(1000.0/freq)+100
sim.sim_time = total_time
# dendritic excitation
ns = h.NetStim()
ns.number = NO_REPS
ns.interval = 1000.0/freq
ns.start = WARM_UP + delta_t - AP_DELAY # ms (most likely) start time of first spike
ex_ex = h.List()
nc_ex = h.List()
for k in range(1,NO_EPSPs+1):
pos = k * 0.1
ex = neuron.h.Exp2Syn(cell.apical(pos))
w = w_ee/NO_EPSPs
ex.tau1 = 0.5 # ms rise time
ex.tau2 = 2 # ms decay time
ex.e = 0 # mV reversal p
# excite postsynaptic cell
nc = h.NetCon(ns,ex,1,0,w)
ex_ex.append(ex)
nc_ex.append(nc)
# inhibition
inh_delay = WARM_UP + delta_t - AP_DELAY + shunt_delay
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)
varec = h.Vector()
varec.record(cell.ais(0.5)._ref_v)
vrec = h.Vector()
vrec.record(cell.soma(0.5)._ref_v)
vinhrec = h.Vector()
pos = 0.18*5.0
vinhrec.record(cell.apical_prox(pos)._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)
grec = h.Vector()
grec.record(exnc._ref_weight[1])
if not shunt_pos == 0:
sim.set_synaptic_recording(switch=False,all=False)
# run simulation
sim.go()
t = np.array(trec)
v = np.array(vrec)
va = np.array(varec)
vinh = np.array(vinhrec)
vd = np.array(vdrec)
vo = np.array(vorec)
vb = np.array(vbrec)
cad = np.array(cadrec)
g = np.array(grec)
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
plot_amp_weight(distal_bAP, AP, shunt_weight_range, savepath, 'distal_bAP')
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')
plot_amp_weight(oblique_bAP, AP, shunt_weight_range, path, 'oblique_bAP')
plot_amp_weight(basal_bAP, AP, shunt_weight_range, path, 'basal_bAP')
my_rawdata['t'] = t
my_rawdata['v'] = v
my_rawdata['va'] = va
my_rawdata['vinh'] = vinh
my_rawdata['vd'] = vd
my_rawdata['vo'] = vo
my_rawdata['vb'] = vb
my_rawdata['cad'] = cad
my_rawdata['g'] = g
rawdata = {'raw_data': my_rawdata}
return rawdata
if __name__ == '__main__':
tables = main()
