NMDAgmax = 10.0 * 1e-3
NMDAalpha = 1.0 / 52.0
NMDAbeta = 1.0 / 343.0
NMDAact = 0.0
NMDAsigma = 0.2
NMDArev = 0.0
GABAgmax = 8.0 * 2e-3
GABAalpha = 1.0 / 1.25
GABAbeta = 1.0 / 18.0
GABAact = -40.0
GABAsigma = 2.0
GABArev = -80.0
# Create model
model = OBModel(mc_pgc_excitation, pgc_mc_inhibition, celsius, AMPAgmax,
AMPAalpha, AMPAbeta, AMPAact, AMPAsigma, AMPArev, NMDAgmax,
NMDAalpha, NMDAbeta, NMDAact, NMDAsigma, NMDArev, GABAgmax,
GABAalpha, GABAbeta, GABAact, GABAsigma, GABArev)
# STIMULATION
# Stim parameters
tstop = 6000
time = range(0, tstop)
hz = 40 #[1, 2, 5, 10, 20, 30, 40] #frequency
c1 = 0.6 #[0.27, 0.315, 0.36, 0.405, 0.45, 0.495, 0.6] #strength
c2 = 0.18 #sets the threshold for firing at 0.18nA for the mitral cells
factor = 0.2 #PG cell input current scaled down to compensate for much higher input resistance
# Input current
input_current = np.ones(
(tstop, )) * [np.cos((t / 1000.0) * (2 * np.pi * hz))
def runOB(frequency,ExFactor,InhFactor,PGFactor,record_vars,syn_vars,directory,filename):
celsius = 35
AMPAgmax = ExFactor*2e-3
AMPAalpha = 1.0
AMPAbeta = 1.0/5.5
AMPAact = 0.0
AMPAsigma = 0.2
AMPArev = 0.0
NMDAgmax = ExFactor*1e-3
NMDAalpha = 1.0/52.0
NMDAbeta = 1.0/343.0
NMDAact = 0.0
NMDAsigma = 0.2
NMDArev = 0.0
GABAgmax = InhFactor*2e-3
GABAalpha = 1.0/1.25
GABAbeta = 1.0/18.0
GABAact = -40.0
GABAsigma = 2.0
GABArev = -80.0
# Circuit 4
mc_pgc_excitation = True
pgc_mc_inhibition = True
pgc_stim = True
# STIMULATION
# Stim parameters
tstop = 3000
time = range(0, tstop)
strength = 0.45
c2 = 0.18 #sets the threshold for firing at 0.18nA for the mitral cells
# Create model
model = OBModel(mc_pgc_excitation, pgc_mc_inhibition, celsius, AMPAgmax, AMPAalpha, AMPAbeta, AMPAact, AMPAsigma, AMPArev, NMDAgmax, NMDAalpha, NMDAbeta, NMDAact, NMDAsigma, NMDArev, GABAgmax, GABAalpha, GABAbeta, GABAact, GABAsigma, GABArev)
# Input current
input_current = np.ones((tstop,))*[np.cos((t/1000.0)*(2*np.pi*frequency)) for t in time]*c2 + strength
input_current[0:500] = 0.0
# PGC stimulation
pgc_input_current = (np.ones((tstop,))*[np.cos((t/1000.0)*(2*np.pi*frequency)) for t in time]*c2 + strength)*PGFactor
pgc_input_current[0:500] = 0.0
# Record membrane potential and time
variables = model.record_membranept(record_vars)
variables = model.record_time(variables)
# Record synaptic current
synvariables = model.record_syn_currents(syn_vars)
# Run model
model.run(variables, tstop, input_current, pgc_input_current, pgc_stim)
# Calculate spike times and latency
vec1 = variables[0]
l = np.array(vec1)
MC_spiketimes_list = model.mc_soma_spike_times(l, threshold = 0)
if len(MC_spiketimes_list) > 0:
MC_first_spike_latency = model.MC_first_spike_latency(MC_spiketimes_list, a = 500)
#MC_interspike_freq = model.MC_spike_frequencies(MC_spiketimes_list)
# PLOTTING AND SAVING
# Time vector
t_vec = variables[-1]
# Plotting synaptic currents
if len(syn_vars) is 1:
model.plotSynapticCurrent_mcGABA(t_vec, synvariables[0], "Synaptic Current at mcGABA", directory +filename + "SC_mcGABA.png")
if len(syn_vars) is 2:
model.plotSynapticCurrent_pgAMPA(t_vec, synvariables[0], "Synaptic Current at pgAMPA", directory +filename + "SC_pgAMPA.png")
model.plotSynapticCurrent_pgNMDA(t_vec, synvariables[1], "Synaptic Current at pgNMDA", directory +filename + "SC_pgNMDA.png")
if len(syn_vars) is 3:
model.plotSynapticCurrent_mcGABA(t_vec, synvariables[0], "Synaptic Current at mcGABA", directory + filename +"SC_mcGABA.png")
model.plotSynapticCurrent_pgAMPA(t_vec, synvariables[1], "Synaptic Current at pgAMPA", directory + filename +"SC_pgAMPA.png")
model.plotSynapticCurrent_pgNMDA(t_vec, synvariables[2], "Synaptic Current at pgNMDA", directory + filename +"SC_pgNMDA.png")
# Save the spiketimes and latency
for i,x in enumerate(record_vars):
variable = variables[i]
np.save(directory + filename +x, variable)
np.save(directory + filename + "MC_Spiketimes", MC_spiketimes_list)
#if len(MC_spiketimes_list) > 0:
np.save(directory + filename + "MC_First_spike_latency", MC_first_spike_latency)
#if len(MC_spiketimes_list) == 0:
# np.save(directory + filename + "MC_First_spike_latency", 0)
plt.close("all")