def placeNMDA(location, conductance):
eSynlist.append(h.ProbAMPANMDA2_RATIO(float(location)))
eSynlist[-1].gmax = conductance
eSynlist[-1].mgVoltageCoeff = 0.08
eNetconlist.append(h.NetCon(E_vcs[-1], eSynlist[-1]))
eNetconlist[-1].weight[0] = 1
eNetconlist[-1].delay = 0
def simulate_single_param(args):
"""
Simulates a specific input parameter vector
:param args: The parameters for the simulation:
The list of excitatory presynpatic inputs,
The list of inhibitory presynpatic inputs,
and the input parameter dictionary
:returns: The voltage trace of the simulation
"""
from neuron import h
from neuron import gui
h.load_file("nrngui.hoc")
h.load_file("import3d.hoc")
param_dict = args
h.dt = 0.025
h("create soma")
h("access soma")
h("nseg = 1")
h("L = 20")
h("diam = 20")
h("insert pas")
h("cm = 1")
h("Ra = 100")
h("forall nseg = 1")
h("g_pas = 0.00005")
h("forall e_pas = -70")
exec('h("tstop = {}")'.format(simulation_length))
(e_ns, e_pc, e_syn) = (None, None, None)
(i_ns, i_pc, i_syn) = (None, None, None)
e_ns = h.NetStim()
e_ns.interval = 1
e_ns.number = 1
e_ns.start = 100
e_ns.noise = 0
e_syn = h.ProbAMPANMDA2_RATIO(0.5)
e_syn.gmax = 1
e_syn.mgVoltageCoeff = 0.08
e_pc = h.NetCon(e_ns, e_syn)
e_pc.weight[0] = 1
e_pc.delay = 0
i_ns = h.NetStim()
i_ns.interval = 1
i_ns.number = 1
i_ns.start = 100
i_ns.noise = 0
i_syn = h.ProbUDFsyn2_lark(0.5)
i_syn.tau_r = 0.18
i_syn.tau_d = 5
i_syn.e = -80
i_syn.Dep = 0
i_syn.Fac = 0
i_syn.Use = 0.6
i_syn.u0 = 0
i_syn.gmax = 1
i_pc = (h.NetCon(i_ns, i_syn))
i_pc.weight[0] = 1
i_pc.delay = 0
delaysVoltageVector = {}
delayDiff = 1
h.finitialize()
nmda_cond = param_dict[0]
gaba_cond = param_dict[1]
delay = param_dict[2]
start = time.time()
e_syn.gmax = nmda_cond
i_syn.gmax = gaba_cond
i_ns.start = 100 + delay
voltageVector = h.Vector()
timeVector = h.Vector()
timeVector.record(h._ref_t)
voltageVector.record(eval("h.soma(0.5)._ref_v"))
h.run()
timeVector = np.array(timeVector)
voltageVector = np.array(voltageVector)
trace = {}
trace['T'] = timeVector
trace['V'] = np.array(voltageVector)
del voltageVector, timeVector
return trace
0) #connect the end of the soma to the start of the dendrite
# set number of segement
h("forall { nseg = int((L/(0.1*lambda_f(100))+0.9)/2)*2 + 1 }")
h.define_shape()
#########################
# # Set up experiment # #
#########################
#We create 20 AMPA_NMDA synapses:
hotspot_NMDA_synapses = []
hotspot_NMDA_netcons = []
hotspot_NMDA_netstims = []
for j in range(20):
hotspot_NMDA_synapses.append(h.ProbAMPANMDA2_RATIO(0.6, sec=h.dend))
hotspot_NMDA_netstims.append(h.NetStim(0.5, sec=h.dend))
hotspot_NMDA_netcons.append(
h.NetCon(hotspot_NMDA_netstims[j], hotspot_NMDA_synapses[j]))
hotspot_NMDA_synapses[j].tau_r_AMPA = 0.33 # AMPA rise time
hotspot_NMDA_synapses[j].tau_d_AMPA = 1 # AMPA decay time
hotspot_NMDA_synapses[j].e = 0
hotspot_NMDA_synapses[j].tau_r_NMDA = 0.23 # NMDA rise time
hotspot_NMDA_synapses[j].tau_d_NMDA = 55 # NMDA decay time
hotspot_NMDA_netcons[j].weight[0] = 0.5 # strength of the synapse
hotspot_NMDA_netstims[j].number = 9e9 # number of synaptic activation
hotspot_NMDA_netstims[j].noise = 1 # randomness
hotspot_NMDA_netstims[
def PutSyns(comp_obj, syn_locs, syn_type_string, weight=None):
"""
Arguments (by order):
- section object on which to put synapses
- array of locations of synapses along the given section
- string indicating which synapse (exc / inh)
Outputs:
- synapses: array of synapse objects
- netstim: array of netstims
- netcon: array of netcons
"""
synapses, netstim, netcon = [], [], []
for loc in syn_locs:
if syn_type_string == 'exc':
if not weight:
weight = 0.4 # Default value for exc. synapses
synapses.append(h.ProbAMPANMDA2_RATIO(
loc, sec=comp_obj)) # Go back to _RATIO!
netstim.append(h.NetStim(loc, sec=comp_obj))
netcon.append(h.NetCon(netstim[-1], synapses[-1]))
# synapses[-1].mgVoltageCoeff = 0.08
netstim[-1].number = 1
netstim[-1].interval = 1
netstim[-1].noise = 0
netstim[-1].start = exc_tstart
netcon[-1].weight[
0] = weight # Literature says ~30pS for 1 NMDAR which is 0.03 here (here it's [nS])
netcon[-1].delay = 0
elif syn_type_string == 'inh':
if not weight:
weight = 0.5 # Default value for inh. synapses
synapses.append(h.ProbUDFsyn2_lark(
loc, sec=comp_obj)) # Go back to _lark!
netstim.append(h.NetStim(loc, sec=comp_obj))
netcon.append(h.NetCon(netstim[-1], synapses[-1]))
synapses[-1].tau_r = 0.18
synapses[-1].tau_d = 5
synapses[-1].e = -80
synapses[-1].Dep = 0
synapses[-1].Fac = 0
synapses[-1].Use = 0.25
synapses[-1].u0 = 0
synapses[
-1].gmax = 0.001 # don't touch - weight conversion factor to (us) times conductance in nS
netstim[-1].number = 1
netstim[-1].interval = 1
netstim[-1].start = inh_tstart
netstim[-1].noise = 0
netcon[-1].weight[0] = weight
netcon[-1].delay = 0
return synapses, netstim, netcon
dend.connect(soma, 1, 0) #connect the end of the soma to the start of the dendrite
# set number of segement
h("forall { nseg = int((L/(0.1*lambda_f(100))+0.9)/2)*2 + 1 }")
h.define_shape()
#########################
# # Set up experiment # #
#########################
#We create 20 AMPA_NMDA synapses:
hotspot_NMDA_synapses = []
hotspot_NMDA_netcons = []
hotspot_NMDA_netstims = []
for j in range(20):
hotspot_NMDA_synapses.append(h.ProbAMPANMDA2_RATIO(dend(0.6)))
hotspot_NMDA_netstims.append(h.NetStim())
hotspot_NMDA_netcons.append(h.NetCon(hotspot_NMDA_netstims[j], hotspot_NMDA_synapses[j]))
hotspot_NMDA_synapses[j].tau_r_AMPA = 0.33 # AMPA rise time
hotspot_NMDA_synapses[j].tau_d_AMPA = 1 # AMPA decay time
hotspot_NMDA_synapses[j].e=0
hotspot_NMDA_synapses[j].tau_r_NMDA = 0.23 # NMDA rise time
hotspot_NMDA_synapses[j].tau_d_NMDA = 55 # NMDA decay time
hotspot_NMDA_netcons[j].weight[0]= 0.5 # strength of the synapse
hotspot_NMDA_netstims[j].number = 9e9 # number of synaptic activation
hotspot_NMDA_netstims[j].noise = 1 # randomness
hotspot_NMDA_netstims[j].interval = 50 # mean time between spikes |50 ms = 20 Hz|
def simulate_single_param(args):
"""
Simulates a specific input parameter vector
:param args: The parameters for the simulation:
The list of excitatory presynpatic inputs,
The list of inhibitory presynpatic inputs,
and the input parameter dictionary
:returns: The voltage trace of the simulation
"""
from neuron import h
from neuron import gui
h.load_file("nrngui.hoc")
h.load_file("import3d.hoc")
E_events_list = args[0]
I_events_list = args[1]
param_dict = args[2]
action_potential_at_soma = pickle.load(open('action_potential_at_soma.pickle', 'rb'))
hoc_code = '''h("create soma")
h("access soma")
h("nseg = 1")
h("L = 20")
h("diam = 20")
h("insert pas")
h("cm = 1")
h("Ra = 150")
h("forall nseg = 1")
h("forall e_pas = -90")
h("soma insert Ca_LVAst")
h("soma insert Ca_HVA")
h("soma insert SKv3_1")
h("soma insert SK_E2 ")
h("soma insert NaTa_t")
h("soma insert CaDynamics_E2")
h("soma insert Im ")
h("soma insert Ih")
h("ek = -85")
h("ena = 50")
h("gIhbar_Ih = 0.0128597")
h("g_pas = 1.0 / 12000 ")
h("celsius = 36")
'''
exec(hoc_code)
exec('h("tstop = {}")'.format(simulation_length))
exec('h("v_init = {}")'.format(v_init))
im = h.Impedance()
h("access soma")
im.loc(0.5)
im.compute(0)
Ri = im.input(0.5)
h("access soma")
h("nseg = 1")
eSynlist = []
eNetconlist = []
iSynlist = []
iNetconlist = []
E_vcs = []
I_vcs = []
eSynlist.append(h.ProbAMPANMDA2_RATIO(0.5))
eSynlist[-1].gmax = 0.0004
eSynlist[-1].mgVoltageCoeff = 0.08
iSynlist.append(h.ProbUDFsyn2_lark(0.5))
iSynlist[-1].tau_r = 0.18
iSynlist[-1].tau_d = 5
iSynlist[-1].e = - 80
iSynlist[-1].gmax = 0.001
semitrial_start = time.time()
for key in param_dict.keys():
if key is not "rate_E" and key is not "rate_I":
h(key + " = " + str(param_dict[key]))
E_events = np.array(E_events_list)[np.argmin(np.abs(np.array(E_events_list).mean(axis=1) - param_dict["rate_E"] / 1000.0))]
I_events = np.array(I_events_list)[np.argmin(np.abs(np.array(I_events_list).mean(axis=1) - param_dict["rate_I"] / 1000.0))]
E_vcs_events = []
I_vcs_events = []
E_vcs = h.VecStim()
I_vcs = h.VecStim()
I_vcs_events.append(h.Vector())
events = np.where(I_events[:])[0] + 100
for event in events:
I_vcs_events[-1].append(event)
E_vcs_events.append(h.Vector())
events = np.where(E_events[:])[0] + 100
for event in events:
E_vcs_events[-1].append(event)
eNetconlist = h.NetCon(E_vcs, eSynlist[-1])
eNetconlist.weight[0] = 1
eNetconlist.delay = 0
E_vcs.play(E_vcs_events[-1])
iNetconlist = h.NetCon(I_vcs, iSynlist[-1])
iNetconlist.weight[0] = 1
iNetconlist.delay = 0
I_vcs.play(I_vcs_events[-1])
### Set the protocol
h.finitialize()
### Simulate!
prev_voltageVector = h.Vector()
prev_voltageVector.record(eval("h.soma(0.5)._ref_v"))
prev_timeVector = h.Vector()
prev_timeVector.record(h._ref_t)
h.tstop = 160
h.run()
h.tstop = 200
voltageVector = h.Vector()
voltageVector.record(eval("h.soma(0.5)._ref_v"))
timeVector = h.Vector()
timeVector.record(h._ref_t)
# Simulate AP
h('''objref clamp
soma clamp = new SEClamp(.5)
{clamp.dur1 = 1e9 clamp.rs=1e9}
''')
active_timeVector = h.Vector(np.arange(0, h.tstop, 0.025))
active_ones = np.array([float(1e9)] * len(active_timeVector))
play_vector = np.array([0.0] * len(active_timeVector))
action_potential_neuron_vector = h.Vector(list(np.maximum(np.array(prev_voltageVector)[int(150 / 0.025) : int(154 / 0.025)], attenuate_action_potential(np.array(np.copy(action_potential_at_soma)), param_dict["percentage_AP"]))))
active_ones[int(ap_time / 0.025) : int((ap_time + 4) / 0.025)] = 0.00001
play_vector[int(ap_time / 0.025) : int((ap_time + 4) / 0.025)] = action_potential_neuron_vector
active_ones = h.Vector(active_ones)
play_vector = h.Vector(play_vector)
play_vector.play(h.clamp._ref_amp1, active_timeVector, 0)
active_ones.play(h.clamp._ref_rs, active_timeVector, 0)
h.run()
timeVector = np.array(timeVector)
voltageVector = np.array(voltageVector)
trace = {}
trace['T'] = timeVector[4000:]
trace['V'] = np.array(voltageVector)[4000:]
h.clamp = None
active_ones = None
play_vector = None
del voltageVector, timeVector, prev_voltageVector, prev_timeVector, action_potential_neuron_vector
return trace
from neuron import gui
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
##=================== creating cell object ===========================
h.load_file("import3d.hoc")
morphology_file = "morphologies/cell1.asc"
h.load_file("models/L5PCbiophys3.hoc")
h.load_file("models/L5PCtemplate.hoc")
L5PC = h.L5PCtemplate(morphology_file)
##==================== create synapses ===========================
#NMDA synapse
NMDA_synapse = h.ProbAMPANMDA2_RATIO(0.5, sec=L5PC.soma[0])
NMDA_netstim = h.NetStim(0.5, sec=L5PC.soma[0])
NMDA_netcons = h.NetCon(NMDA_netstim, NMDA_synapse)
NMDA_synapse.tau_r_AMPA = 0.33
NMDA_synapse.tau_d_AMPA = 1
NMDA_synapse.e = 0
NMDA_synapse.mgVoltageCoeff = 0.08
NMDA_netstim.number = 1
NMDA_netstim.noise = 0
NMDA_netstim.start = 100
NMDA_netcons.weight[0] = 0.9
#GABA synapse
GABA_synapse = h.Exp2Syn(0.5, sec=L5PC.soma[0])