import matplotlib as mpl
mpl.use('tkagg')
from neuron import h,gui
from Cereb_GrC_adapting import Grc_adapting
import multiprocessing
import numpy as np
import matplotlib.pyplot as plt
cell = Grc_adapting(1)
h.nrncontrolmenu()
time_step = h.CVode()
time_step.active(0) #0 fixed step, 1 variable time step
cpu = multiprocessing.cpu_count()
h.load_file("parcom.hoc")
p = h.ParallelComputeTool()
if cpu > 8:
p.change_nthread(8,1)
print('Maximum 8 threads')
else:
p.change_nthread(cpu,1)
print('N° of treads', cpu)
p.multisplit(1)
h('load_file("vm.ses")')
stimdata = dict()
stimdata['timeglobal'] = 2500
synapsesdata = dict()
#number of AMPA syn, NMDA syn, dend number from 0 to 3
cell.createsyn(1, 1, [0,1], [0,1])
#Mossy syn
spk_stim_mossy = []
for protocol_name, frequency in [('step1', 200), ('step2', 100), ('step3', 50), ('step4', 25), ('step5', 16.6), ('step6', 12.5)]:
spk_stim = h.NetStim()
spk_stim.interval = frequency
spk_stim.number = int(1000/frequency)
spk_stim.noise = 0
spk_stim.start = 100
spk_stim_2 = h.NetStim()
spk_stim_2.interval = 10
spk_stim_2.number = 25
spk_stim_2.noise = 0
spk_stim_2.start = 1100
spk_stim_3 = h.NetStim()
spk_stim_3.interval = frequency
spk_stim_3.number = int(1000/frequency)
spk_stim_3.noise = 0
spk_stim_3.start = 1350
spk_stim_4 = h.NetStim()
spk_stim_4.interval = 10
spk_stim_4.number = 25
spk_stim_4.noise = 0
spk_stim_4.start = 2350
spk_stim_mossy.append(spk_stim)
spk_stim_mossy.append(spk_stim_2)
spk_stim_mossy.append(spk_stim_3)
spk_stim_mossy.append(spk_stim_4)
spk_nc_pfsyn = []
spk_nc_pfsyn_nmda = []
for m in range(int(len(spk_stim_mossy))):
spk_nc_pfsyn.append([h.NetCon(spk_stim_mossy[m],mossy.input,0,0.1,1) for mossy in cell.MF_GrC])
spk_nc_pfsyn_nmda.append([h.NetCon(spk_stim_mossy[m],mossynmda.input,0,0.1,1) for mossynmda in cell.MF_GrC_mossy])
h.dt = 0.025
h.celsius = 32
h.tstop = stimdata['timeglobal']
h.v_init = -70
def initialize():
h.finitialize()
h.run()
initialize()
#save files
np.savetxt('02_syn_soma_' + str(protocol_name) + '.txt', np.column_stack((np.array(cell.time_vector), np.array(cell.vm_soma))), delimiter = ' ')
img = plt.plot(np.array(cell.time_vector), np.array(cell.vm_soma))
plt.xlabel("Time")
plt.ylabel("Amplitude")
plt.savefig('02_syn_soma_' + str(protocol_name) + '.eps')
plt.close()
quit()