def measure_fi (gid, pop_name, v_init, env, cell_dict={}):
biophys_cell = init_biophys_cell(env, pop_name, gid, register_cell=False, cell_dict=cell_dict)
hoc_cell = biophys_cell.hoc_cell
soma = list(hoc_cell.soma)[0]
h.dt = 0.025
prelength = 1000.0
mainlength = 2000.0
tstop = prelength+mainlength
stimdur = 1000.0
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = 0.2
h('objref tlog, Vlog, spikelog')
h.tlog = h.Vector()
h.tlog.record (h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record (soma(0.5)._ref_v)
h.spikelog = h.Vector()
nc = biophys_cell.spike_detector
nc.record(h.spikelog)
h.tstop = tstop
frs = []
stim_amps = [stim1.amp]
for it in range(1, 9):
neuron_utils.simulate(v_init, prelength, mainlength)
logger.info("fi_test: stim1.amp = %g spikelog.size = %d\n" % (stim1.amp, h.spikelog.size()))
stim1.amp = stim1.amp + 0.1
stim_amps.append(stim1.amp)
frs.append(h.spikelog.size())
h.spikelog.clear()
h.tlog.clear()
h.Vlog.clear()
results = {'FI_curve_amplitude': np.asarray(stim_amps, dtype=np.float32),
'FI_curve_frequency': np.asarray(frs, dtype=np.float32) }
env.synapse_attributes.del_syn_id_attr_dict(gid)
if gid in env.biophys_cells[pop_name]:
del env.biophys_cells[pop_name][gid]
return results
def measure_passive (gid, pop_name, v_init, env, prelength=1000.0, mainlength=2000.0, stimdur=500.0, cell_dict={}):
biophys_cell = init_biophys_cell(env, pop_name, gid, register_cell=False, cell_dict=cell_dict)
hoc_cell = biophys_cell.hoc_cell
h.dt = env.dt
tstop = prelength+mainlength
soma = list(hoc_cell.soma)[0]
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = -0.1
h('objref tlog, Vlog')
h.tlog = h.Vector()
h.tlog.record (h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record (soma(0.5)._ref_v)
h.tstop = tstop
Rin = h.rn(hoc_cell)
neuron_utils.simulate(v_init, prelength, mainlength)
## compute membrane time constant
vrest = h.Vlog.x[int(h.tlog.indwhere(">=",prelength-1))]
vmin = h.Vlog.min()
vmax = vrest
## the time it takes the system's step response to reach 1-1/e (or
## 63.2%) of the peak value
amp23 = 0.632 * abs (vmax - vmin)
vtau0 = vrest - amp23
tau0 = h.tlog.x[int(h.Vlog.indwhere ("<=", vtau0))] - prelength
results = {'Rin': np.asarray([Rin], dtype=np.float32),
'vmin': np.asarray([vmin], dtype=np.float32),
'vmax': np.asarray([vmax], dtype=np.float32),
'vtau0': np.asarray([vtau0], dtype=np.float32),
'tau0': np.asarray([tau0], dtype=np.float32)
}
env.synapse_attributes.del_syn_id_attr_dict(gid)
if gid in env.biophys_cells[pop_name]:
del env.biophys_cells[pop_name][gid]
return results
def fi_test(template_class, tree, v_init):
cell = cells.make_neurotree_cell(template_class, neurotree_dict=tree)
soma = list(cell.soma)[0]
h.dt = 0.025
prelength = 1000.0
mainlength = 2000.0
tstop = prelength + mainlength
stimdur = 1000.0
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = 0.2
h.tlog = h.Vector()
h.tlog.record(h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record(soma(0.5)._ref_v)
h.spikelog = h.Vector()
nc = h.NetCon(soma(0.5)._ref_v, h.nil)
nc.threshold = -20.0
nc.record(h.spikelog)
h.tstop = tstop
frs = []
stim_amps = [stim1.amp]
for it in range(1, 9):
neuron_utils.simulate(v_init, prelength, mainlength)
print("fi_test: stim1.amp = %g spikelog.size = %d\n" %
(stim1.amp, h.spikelog.size()))
stim1.amp = stim1.amp + 0.1
stim_amps.append(stim1.amp)
frs.append(h.spikelog.size())
h.spikelog.clear()
h.tlog.clear()
h.Vlog.clear()
f = open("HIPPCell_fi_results.dat", 'w')
for (fr, stim_amp) in zip(frs, stim_amps):
f.write("%g %g\n" % (stim_amp, fr))
f.close()
def passive_test(template_class, tree, v_init):
cell = cells.make_neurotree_cell(template_class, neurotree_dict=tree)
h.topology()
h.dt = 0.025
prelength = 1000
mainlength = 2000
tstop = prelength + mainlength
stimdur = 500.0
soma = list(cell.soma)[0]
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = -0.1
h.tlog = h.Vector()
h.tlog.record(h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record(soma(0.5)._ref_v)
h.tstop = tstop
neuron_utils.simulate(v_init, prelength, mainlength)
## compute membrane time constant
vrest = h.Vlog.x[int(h.tlog.indwhere(">=", prelength - 1))]
vmin = h.Vlog.min()
vmax = vrest
## the time it takes the system's step response to reach 1-1/e (or
## 63.2%) of the peak value
amp23 = 0.632 * abs(vmax - vmin)
vtau0 = vrest - amp23
tau0 = h.tlog.x[int(h.Vlog.indwhere("<=", vtau0))] - prelength
f = open("HIPPCell_passive_results.dat", 'w')
f.write("DC input resistance: %g MOhm\n" % h.rn(cell))
f.write("vmin: %g mV\n" % vmin)
f.write("vtau0: %g mV\n" % vtau0)
f.write("tau0: %g ms\n" % tau0)
f.close()
def measure_psp(gid,
pop_name,
presyn_name,
syn_mech_name,
swc_type,
env,
v_init,
erev,
syn_layer=None,
weight=1,
syn_count=1,
load_weights=False,
cell_dict={}):
biophys_cell = init_biophys_cell(env,
pop_name,
gid,
register_cell=False,
load_weights=load_weights,
cell_dict=cell_dict)
synapses.config_biophys_cell_syns(env,
gid,
pop_name,
insert=True,
insert_netcons=True,
insert_vecstims=True)
hoc_cell = biophys_cell.hoc_cell
h.dt = env.dt
prelength = 200.0
mainlength = 50.0
rules = {'sources': [presyn_name]}
if swc_type is not None:
rules['swc_types'] = [swc_type]
if syn_layer is not None:
rules['layers'] = [syn_layer]
syn_attrs = env.synapse_attributes
syn_filters = get_syn_filter_dict(env, rules=rules, convert=True)
syns = syn_attrs.filter_synapses(biophys_cell.gid, **syn_filters)
print("total number of %s %s synapses: %d" %
(presyn_name, swc_type if swc_type is not None else "", len(syns)))
stimvec = h.Vector()
stimvec.append(prelength + 1.)
count = 0
target_syn_pps = None
for target_syn_id, target_syn in iter(syns.items()):
target_syn_pps = syn_attrs.get_pps(gid, target_syn_id, syn_mech_name)
target_syn_nc = syn_attrs.get_netcon(gid, target_syn_id, syn_mech_name)
target_syn_nc.weight[0] = weight
setattr(target_syn_pps, 'e', erev)
vs = target_syn_nc.pre()
vs.play(stimvec)
if syn_count <= count:
break
count += 1
if target_syn_pps is None:
raise RuntimeError(
"measure_psp: Unable to find %s %s synaptic point process" %
(presyn_name, swc_type))
sec = target_syn_pps.get_segment().sec
v_rec = make_rec('psp',
pop_name,
gid,
biophys_cell.hoc_cell,
sec=sec,
dt=env.dt,
loc=0.5,
param='v')
h.tstop = mainlength + prelength
h('objref nil, tlog, ilog')
h.tlog = h.Vector()
h.tlog.record(h._ref_t)
h.ilog = h.Vector()
h.ilog.record(target_syn_pps._ref_i)
neuron_utils.simulate(v_init, prelength, mainlength)
vec_v = np.asarray(v_rec['vec'].to_python())
vec_i = np.asarray(h.ilog.to_python())
vec_t = np.asarray(h.tlog.to_python())
idx = np.where(vec_t >= prelength)[0]
vec_v = vec_v[idx][1:]
vec_t = vec_t[idx][1:]
vec_i = vec_i[idx][1:]
i_peak_index = np.argmax(np.abs(vec_i))
i_peak = vec_i[i_peak_index]
v_peak = vec_v[i_peak_index]
amp_v = abs(v_peak - vec_v[0])
amp_i = abs(i_peak - vec_i[0])
print("measure_psp: v0 = %f v_peak = %f (at t %f)" %
(vec_v[0], v_peak, vec_t[i_peak_index]))
print("measure_psp: i_peak = %f (at t %f)" % (i_peak, vec_t[i_peak_index]))
print("measure_psp: amp_v = %f amp_i = %f" % (amp_v, amp_i))
results = {
'%s %s PSP' % (presyn_name, syn_mech_name):
np.asarray([amp_v], dtype=np.float32),
'%s %s PSP i' % (presyn_name, syn_mech_name):
np.asarray(vec_i, dtype=np.float32),
'%s %s PSP v' % (presyn_name, syn_mech_name):
np.asarray(vec_v, dtype=np.float32),
'%s %s PSP t' % (presyn_name, syn_mech_name):
np.asarray(vec_t, dtype=np.float32)
}
env.synapse_attributes.del_syn_id_attr_dict(gid)
if gid in env.biophys_cells[pop_name]:
del env.biophys_cells[pop_name][gid]
return results
def measure_psc(gid,
pop_name,
presyn_name,
env,
v_init,
v_holding,
load_weights=False,
cell_dict={}):
biophys_cell = init_biophys_cell(env,
pop_name,
gid,
register_cell=False,
load_weights=load_weights,
cell_dict=cell_dict)
hoc_cell = biophys_cell.hoc_cell
h.dt = env.dt
stimdur = 1000.0
tstop = stimdur
tstart = 0.
soma = list(hoc_cell.soma)[0]
se = h.SEClamp(soma(0.5))
se.rs = 10
se.dur = stimdur
se.amp1 = v_holding
h('objref nil, tlog, ilog, Vlog')
h.tlog = h.Vector()
h.tlog.record(h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record(soma(0.5)._ref_v)
h.ilog = h.Vector()
ilog.record(se._ref_i)
h.tstop = tstop
neuron_utils.simulate(v_init, 0., stimdur)
vec_i = h.ilog.to_python()
vec_v = h.Vlog.to_python()
vec_t = h.tlog.to_python()
idx = np.where(vec_t > tstart)[0]
vec_i = vec_i[idx]
vec_v = vec_v[idx]
vec_t = vec_t[idx]
t_holding = vec_t[0]
i_holding = vec_i[0]
i_peak = np.max(np.abs(vec_i[1:]))
peak_index = np.where(np.abs(vec_i) == i_peak)[0][0]
t_peak = vec_t[peak_index]
logger.info("measure_psc: t_peak = %f i_holding = %f i_peak = %f" %
(t_peak, i_holding, i_peak))
amp_i = abs(i_peak - i_holding) * 1000
logger.info("measure_psc: amp_i = %f" % amp_i)
return amp_i
def measure_ap_rate(gid,
pop_name,
v_init,
env,
prelength=1000.0,
mainlength=3000.0,
stimdur=1000.0,
stim_amp=0.2,
minspikes=50,
maxit=5,
cell_dict={}):
biophys_cell = init_biophys_cell(env,
pop_name,
gid,
register_cell=False,
cell_dict=cell_dict)
hoc_cell = biophys_cell.hoc_cell
tstop = prelength + mainlength
soma = list(hoc_cell.soma)[0]
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = stim_amp
h('objref nil, tlog, Vlog, spikelog')
h.tlog = h.Vector()
h.tlog.record(h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record(soma(0.5)._ref_v)
h.spikelog = h.Vector()
nc = biophys_cell.spike_detector
nc.record(h.spikelog)
logger.info(f"ap_rate_test: spike threshold is {nc.threshold}")
h.tstop = tstop
it = 1
## Increase the injected current until at least maxspikes spikes occur
## or up to maxit steps
while (h.spikelog.size() < minspikes):
logger.info(f"ap_rate_test: iteration {it}")
h.dt = env.dt
neuron_utils.simulate(v_init, prelength, mainlength)
if ((h.spikelog.size() < minspikes) & (it < maxit)):
logger.info(
f"ap_rate_test: stim1.amp = {stim1.amp:.2f} spikelog.size = {h.spikelog.size()}"
)
stim1.amp = stim1.amp + 0.1
h.spikelog.clear()
h.tlog.clear()
h.Vlog.clear()
it += 1
else:
break
logger.info(
f"ap_rate_test: stim1.amp = {stim1.amp:.2f} spikelog.size = {h.spikelog.size()}"
)
isivect = h.Vector(h.spikelog.size() - 1, 0.0)
tspike = h.spikelog.x[0]
for i in range(1, int(h.spikelog.size())):
isivect.x[i - 1] = h.spikelog.x[i] - tspike
tspike = h.spikelog.x[i]
isimean = isivect.mean()
isivar = isivect.var()
isistdev = isivect.stdev()
isilast = int(isivect.size()) - 1
if (isivect.size() > 10):
isi10th = 10
else:
isi10th = isilast
## Compute the last spike that is largest than the first one.
## This is necessary because some models generate spike doublets,
## (i.e. spike with very short distance between them, which confuse the ISI statistics.
isilastgt = int(isivect.size()) - 1
while (isivect.x[isilastgt] < isivect.x[1]):
isilastgt = isilastgt - 1
if (not (isilastgt > 0)):
isivect.printf()
raise RuntimeError("Unable to find ISI greater than first ISI")
results = {
'spike_count':
np.asarray([h.spikelog.size()], dtype=np.uint32),
'FR_mean':
np.asarray([1.0 / isimean], dtype=np.float32),
'ISI_mean':
np.asarray([isimean], dtype=np.float32),
'ISI_var':
np.asarray([isivar], dtype=np.float32),
'ISI_stdev':
np.asarray([isistdev], dtype=np.float32),
'ISI_adaptation_1':
np.asarray([isivect.x[0] / isimean], dtype=np.float32),
'ISI_adaptation_2':
np.asarray([isivect.x[0] / isivect.x[isilast]], dtype=np.float32),
'ISI_adaptation_3':
np.asarray([isivect.x[0] / isivect.x[isi10th]], dtype=np.float32),
'ISI_adaptation_4':
np.asarray([isivect.x[0] / isivect.x[isilastgt]], dtype=np.float32)
}
env.synapse_attributes.del_syn_id_attr_dict(gid)
if gid in env.biophys_cells[pop_name]:
del env.biophys_cells[pop_name][gid]
return results
def ap_rate_test(template_class, tree, v_init):
cell = cells.make_neurotree_cell(template_class, neurotree_dict=tree)
h.dt = 0.025
prelength = 1000.0
mainlength = 2000.0
tstop = prelength + mainlength
stimdur = 1000.0
soma = list(cell.soma)[0]
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = 0.2
h.tlog = h.Vector()
h.tlog.record(h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record(soma(0.5)._ref_v)
h.spikelog = h.Vector()
nc = h.NetCon(soma(0.5)._ref_v, h.nil)
nc.threshold = -20.0
nc.record(h.spikelog)
h.tstop = tstop
it = 1
## Increase the injected current until at least 60 spikes occur
## or up to 5 steps
while (h.spikelog.size() < 50):
neuron_utils.simulate(v_init, prelength, mainlength)
if ((h.spikelog.size() < 50) & (it < 5)):
print("ap_rate_test: stim1.amp = %g spikelog.size = %d\n" %
(stim1.amp, h.spikelog.size()))
stim1.amp = stim1.amp + 0.1
h.spikelog.clear()
h.tlog.clear()
h.Vlog.clear()
it += 1
else:
break
print("ap_rate_test: stim1.amp = %g spikelog.size = %d\n" %
(stim1.amp, h.spikelog.size()))
isivect = h.Vector(h.spikelog.size() - 1, 0.0)
tspike = h.spikelog.x[0]
for i in range(1, int(h.spikelog.size())):
isivect.x[i - 1] = h.spikelog.x[i] - tspike
tspike = h.spikelog.x[i]
print("ap_rate_test: isivect.size = %d\n" % isivect.size())
isimean = isivect.mean()
isivar = isivect.var()
isistdev = isivect.stdev()
isilast = int(isivect.size()) - 1
if (isivect.size() > 10):
isi10th = 10
else:
isi10th = isilast
## Compute the last spike that is largest than the first one.
## This is necessary because some variants of the model generate spike doublets,
## (i.e. spike with very short distance between them, which confuse the ISI statistics.
isilastgt = int(isivect.size()) - 1
while (isivect.x[isilastgt] < isivect.x[1]):
isilastgt = isilastgt - 1
if (not (isilastgt > 0)):
isivect.printf()
raise RuntimeError(
"Unable to find ISI greater than first ISI: forest_path = %s gid = %d"
% (forest_path, gid))
f = open("HIPPCell_ap_rate_results.dat", 'w')
f.write("## number of spikes: %g\n" % h.spikelog.size())
f.write("## FR mean: %g\n" % (1.0 / isimean))
f.write("## ISI mean: %g\n" % isimean)
f.write("## ISI variance: %g\n" % isivar)
f.write("## ISI stdev: %g\n" % isistdev)
f.write("## ISI adaptation 1: %g\n" % (isivect.x[0] / isimean))
f.write("## ISI adaptation 2: %g\n" % (isivect.x[0] / isivect.x[isilast]))
f.write("## ISI adaptation 3: %g\n" % (isivect.x[0] / isivect.x[isi10th]))
f.write("## ISI adaptation 4: %g\n" %
(isivect.x[0] / isivect.x[isilastgt]))
f.close()
f = open("HIPPCell_voltage_trace.dat", 'w')
for i in range(0, int(h.tlog.size())):
f.write('%g %g\n' % (h.tlog.x[i], h.Vlog.x[i]))
f.close()