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single_compartment_SoldadoMagranerEtAl.py
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single_compartment_SoldadoMagranerEtAl.py
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####################################################################################
# Ca3 pyramidal cell single compartment model of the study:
# "Conditioning by Subthreshold Synaptic Input Changes the Intrinsic Firing Pattern of CA3 Hippocampal Neurons"
# Saray Soldado-Magraner, Federico Brandalise, Suraj Honnuraiah, Michael Pfeiffer, Marie Moulinier, Urs Gerber, Rodney Douglas. JNeurophysiology, 2019.
# Written by Adrian Gutierrez [email protected] and Saray Soldado-Magraner [email protected]
#####################################################################################
from neuron import h, gui
import numpy as np
import matplotlib.pyplot as plt
##Create soma
soma = h.Section(name='soma')
##Modify soma's properties
soma.L = 50.0 #Length of soma (um)
soma.diam = 50.0 #Diameter of soma (um)
h.celsius = 34
h.steps_per_ms= 40 #Default neuron integration
h.dt=0.025
##Insert passive biophysical mechanisms to soma
soma.insert('pas')
soma.Ra = 150.0 #Membrane axial resistance (Ohm/cm^2)
soma.cm = 1.41 #Membrane capacitance
soma.g_pas = 1.0/25370.0 #Leak maximal conductance
##Insert active biophysical mechanisms to soma (inserting .mod files)
for channel in ['na3', 'kdr', 'kap', 'km', 'kd', 'kd_slow',
'cacum', 'cal', 'can', 'cat', 'cagk']:
soma.insert(channel)
###############################################
# Setup model conductances
################################################3
#Which pattern are we testing
pattern = 'iBurst'
soma.gbar_na3= 0.04 # Spiking conductances
soma.gkdrbar_kdr=0.01
soma.gkabar_kap=0.07
gCa= 0.001 # Firing pattern conductances
soma.gcatbar_cat=gCa
soma.gcanbar_can=gCa
soma.gcalbar_cal=gCa
soma.gbar_cagk=0.0001
soma.gbar_km=0.0006
soma.gkdbar_kd=0.00045
soma.gkdbar_kd_slow=0
#Reversal potentials and calcium
soma.depth_cacum = 50.0/2.0 #Set the point where calcium mechanisms act (Soma diameter/2)
soma.ek = -90.0 #Potassium reversal potential (mV)
soma.ena = 55.0 #Sodium reversal potential (mV)
###############################################
# Setup recording vectors
################################################
v_vec = h.Vector() #Membrane potential vector
t_vec = h.Vector() #Time stamp vector
v_vec.record(soma(0.5)._ref_v) #Recording from the soma the desired quantities
t_vec.record(h._ref_t)
###############################################
# Current Clamp
################################################
st = h.IClamp(0.5) #Choose where in the soma to point-stimulate
st.dur = 1000 #Stimulus duration (ms)
st.delay = 100 #Stimulus delay (ms)
st.amp = 1 #Stimulus amplitude (nA)
h.tstop = 1200 #stop the simulation (ms)
##########################################################################################################
# Run the simulation
##########################################################################################################
h.v_init = -65 #Set initializing simulation voltage (mV) at t0
h.finitialize(-65) #Set initializing voltage for all mechanisms in the section
h.fcurrent()
if h.ismembrane('cal'): #start the 'vClamp' by setting e_pas to match v_init (NEURON Book, ch8, p11)
soma.e_pas = soma.v + (soma.ina + soma.ik + soma.ica)/soma.g_pas
else:
soma.e_pas = soma.v + (soma.ina + soma.ik)/soma.g_pas
h.run()
###################################################
# PLOTTING
###################################################
plt.figure(figsize=(12,7))
plt.title(pattern + ' firing pattern (IClamp)', fontweight='bold')
plt.plot(t_vec, v_vec)
plt.ylabel('Membrane voltage (mV)',fontsize=16)
plt.xlabel('Time (ms)', fontsize=16)
plt.show()