def test_psg_fixed():
psg = PoissonSpikeGenerator(population='test', seed=100)
psg.add(node_ids=range(10), firing_rate=5.0, times=(0.0, 3.0))
assert (psg.populations == ['test'])
assert (np.all(psg.node_ids() == list(range(10))))
assert (psg.n_spikes() == 143)
assert (psg.n_spikes(population='test') == 143)
assert (np.allclose(psg.time_range(),
(5.380662350673328, 2986.5205688893295)))
df = psg.to_dataframe()
assert (df.shape == (143, 3))
assert (np.allclose(psg.get_times(node_id=0), [
156.7916, 222.0400, 332.5493, 705.1267, 706.0727, 731.9963, 954.1834,
1303.7542, 1333.1543, 1504.3314, 1948.2045, 1995.1471, 2036.1411,
2059.0835, 2108.6982, 2877.7935
],
atol=1.0e-3))
assert (np.allclose(psg.get_times(node_id=9, population='test'), [
23.3176, 241.7332, 390.1951, 428.2215, 1001.0229, 1056.4003, 2424.8442,
2599.6312, 2640.1228, 2737.9504, 2780.0140, 2885.8020
],
atol=1.0e-3))
def _activate_hln(self, sim, block_interval, firing_rate):
next_block_tstart = (block_interval[1] + 1) * sim.dt # The next time-step
next_block_tstop = next_block_tstart + sim.nsteps_block*sim.dt # The time-step when the next block ends
# This is where you can use the firing-rate of the low-level neurons to generate a set of spike times for the
# next block
if firing_rate != 0.0:
psg = PoissonSpikeGenerator()
psg.add(node_ids=[0], firing_rate=firing_rate, times=(next_block_tstart/1000.0, next_block_tstop/1000.0))
if psg.n_spikes() <= 0:
io.log_info(' _activate_hln: firing rate {} did not produce any spikes'.format(firing_rate))
else:
self._spike_events = psg.get_times(0)
# Update firing rate of bladder afferent neurons
for gid in self._high_level_neurons:
nc = self._netcons[gid]
for t in self._spike_events:
nc.event(t)
else:
io.log_info(' _activate_hln: firing rate 0')
# If pressure is maxxed, update firing rate of EUS motor neurons
# Guarding reflex
# press_change = self._prev_glob_press - self._glob_press
# if self._glob_press > press_thres or press_change > change_thres:
# psg = PoissonSpikeGenerator()
# eus_fr = self._glob_press*10 + press_change*10 # Assumption: guarding reflex
# # depends on current pressure
# # and change from last pressure
# psg.add(node_ids=[0], firing_rate=eus_fr, times=(next_block_tstart, next_block_tstop))
# self._spike_events = psg.get_times(0)
# for gid in self._eus_neurons:
# nc = self._netcons[gid]
# for t in self._spike_events:
# nc.event(t)
################ Activate higher order based on pressure threshold ##############################
# if block_interval[1] % 2000 == 1000: # For fast testing, only add events to every other block
# if False: # For testing
if self._glob_press > self.press_thres:
io.log_info(' updating pag input')
psg = PoissonSpikeGenerator()
print(self.press_thres)
pag_fr = self.press_thres #change
psg.add(node_ids=[0], firing_rate=pag_fr, times=(next_block_tstart/1000.0, next_block_tstop/1000.0))
if psg.n_spikes() <= 0:
io.log_info(' no psg spikes generated by Poisson distritubtion')
self._spike_events = psg.get_times(0)
for gid in self._pag_neurons:
nc = self._netcons[gid]
for t in self._spike_events:
nc.event(t)
def test_psg_fixed():
psg = PoissonSpikeGenerator(population='test', seed=100)
psg.add(node_ids=range(10), firing_rate=5.0, times=(0.0, 3.0))
assert (psg.populations == ['test'])
assert (np.all(psg.node_ids() == list(range(10))))
assert (psg.n_spikes() == 143)
assert (psg.n_spikes(population='test') == 143)
assert (np.allclose(psg.time_range(),
(0.005380662350673328, 2.9865205688893295)))
df = psg.to_dataframe()
assert (df.shape == (143, 3))
assert (np.allclose(psg.get_times(node_id=0), [
0.156, 0.222, 0.332, 0.705, 0.706, 0.731, 0.954, 1.303, 1.333, 1.504,
1.948, 1.995, 2.036, 2.059, 2.108, 2.877
],
atol=1.0e-3))
assert (np.allclose(psg.get_times(node_id=9, population='test'), [
0.0233, 0.241, 0.390, 0.428, 1.001, 1.056, 2.424, 2.599, 2.640, 2.737,
2.780, 2.885
],
atol=1.0e-3))
def test_psg_variable():
times = np.linspace(0.0, 3.0, 1000)
fr = np.exp(-np.power(times - 1.0, 2) / (2 * np.power(.5, 2))) * 5
psg = PoissonSpikeGenerator(population='test', seed=0.0)
psg.add(node_ids=range(10), firing_rate=fr, times=times)
assert (psg.populations == ['test'])
assert (np.all(psg.node_ids() == list(range(10))))
assert (psg.n_spikes() == 59)
assert (np.allclose(psg.time_range(),
(0.13932107933711294, 2.9013003727909172)))
assert (psg.to_dataframe().shape == (59, 3))
assert (np.allclose(psg.get_times(node_id=0),
[0.442, 0.520, 0.640, 1.099, 1.393, 1.725],
atol=1.0e-3))
assert (np.allclose(psg.get_times(node_id=9),
[0.729, 0.885, 1.047, 1.276, 1.543, 1.669, 1.881],
atol=1.0e-3))
def test_psg_variable():
times = np.linspace(0.0, 3.0, 1000)
fr = np.exp(-np.power(times - 1.0, 2) / (2 * np.power(.5, 2))) * 5
psg = PoissonSpikeGenerator(population='test', seed=0.0)
psg.add(node_ids=range(10), firing_rate=fr, times=times)
assert (psg.populations == ['test'])
assert (np.all(psg.node_ids() == list(range(10))))
assert (psg.n_spikes() == 59)
assert (np.allclose(psg.time_range(),
(139.32107933711294, 2901.3003727909172)))
assert (psg.to_dataframe().shape == (59, 3))
assert (np.allclose(
psg.get_times(node_id=0),
[442.8378, 520.3624, 640.3880, 1099.0661, 1393.0794, 1725.6109],
atol=1.0e-3))
assert (np.allclose(psg.get_times(node_id=9), [
729.6267, 885.2469, 1047.7728, 1276.3554, 1543.6557, 1669.9070,
1881.3605
],
atol=1.0e-3))
# ('shock', './12_cell_inputs/shock_spikes.csv'),
# ('bg_pn', '12_cell_inputs/bg_pn_spikes.h5'),
# ('bg_pv', '12_cell_inputs/bg_pv_spikes.h5'),
# ('bg_olm', '12_cell_inputs/bg_olm_spikes.h5')],
# components_dir='biophys_components',
# config_file='config.json',
# compile_mechanisms=False)
psg = PoissonSpikeGenerator(population='bg_pn')
psg.add(
node_ids=range(8), # need same number as cells
firing_rate=1, # 1 spike every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
psg.to_sonata('12_cell_inputs/bg_pn_spikes.h5')
print('Number of background spikes for pn: {}'.format(psg.n_spikes()))
psg = PoissonSpikeGenerator(population='bg_pv')
psg.add(
node_ids=range(2), # need same number as cells
firing_rate=8, # 8 spikes every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
psg.to_sonata('12_cell_inputs/bg_pv_spikes.h5')
print('Number of background spikes for pv: {}'.format(psg.n_spikes()))
psg = PoissonSpikeGenerator(population='bg_olm')
psg.add(
node_ids=range(2), # need same number as cells
firing_rate=2, # 8 spikes every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
# SPIKE TRAINS
t_sim = 40000
#build_env_bionet(base_dir='./',
# network_dir='./network',
# tstop=t_sim, dt=0.1,
# report_vars=['v'],
# components_dir='biophys_components',
# config_file='config.json',
# spikes_inputs=[('bg_pn_c', '2_cell_inputs/bg_pn_c_spikes.h5'),
# ('bg_pv', '2_cell_inputs/bg_pv_spikes.h5')],
# compile_mechanisms=False)
psg = PoissonSpikeGenerator(population='bg_pn_c')
psg.add(
node_ids=range(1), # need same number as cells
firing_rate=6, # 1 spike every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
psg.to_sonata('2_cell_inputs/bg_pn_c_spikes.h5')
print('Number of background spikes for PN_C: {}'.format(psg.n_spikes()))
psg = PoissonSpikeGenerator(population='bg_pv')
psg.add(
node_ids=range(1), # need same number as cells
firing_rate=7.7, # 8 spikes every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
psg.to_sonata('2_cell_inputs/bg_pv_spikes.h5')
print('Number of background spikes for PV: {}'.format(psg.n_spikes()))
distance_range=[0.0, 150.0],
dynamics_params='AMPA_ExcToExc.json',
model_template='exp2syn')
net.build()
net.save(output_dir='network')
thalamus.build()
thalamus.save(output_dir='network')
psg = PoissonSpikeGenerator(population='mthalamus')
psg.add(
node_ids=1, # Have 5 nodes to match mthalamus
firing_rate=8, # 2 Hz
times=(0.0, 1)) # time is in seconds for some reason
psg.to_sonata('virtual_spikes.h5')
print('Number of background spikes: {}'.format(psg.n_spikes()))
from bmtk.utils.sim_setup import build_env_bionet
build_env_bionet(
base_dir='../',
network_dir='./network',
tstop=1000.0,
dt=0.1,
report_vars=['v'],
spikes_inputs=[('mthalamus', 'virtual_spikes.h5')],
#current_clamp={
# 'amp': -0.100,
# 'delay': 250.0,
# 'duration': 200 #200 for bask 600 for pyr
#},
components_dir='../biophys_components',
def build_model():
if os.path.isdir('network'):
shutil.rmtree('network')
if os.path.isdir('2_cell_inputs'):
shutil.rmtree('2_cell_inputs')
seed = 967
random.seed(seed)
np.random.seed(seed)
load()
syn = syn_params_dicts()
# Initialize our network
net = NetworkBuilder("biophysical")
num_inh = [1]
num_exc = [1]
##################################################################################
###################################BIOPHY#########################################
# PN
net.add_nodes(N=1,
pop_name='PyrC',
mem_potential='e',
model_type='biophysical',
model_template='hoc:Cell_C',
morphology=None)
# PV
net.add_nodes(N=1,
pop_name="PV",
mem_potential='e',
model_type='biophysical',
model_template='hoc:basket',
morphology=None)
backgroundPN_C = NetworkBuilder('bg_pn_c')
backgroundPN_C.add_nodes(N=1,
pop_name='tON',
potential='exc',
model_type='virtual')
backgroundPV = NetworkBuilder('bg_pv')
backgroundPV.add_nodes(N=2,
pop_name='tON',
potential='exc',
model_type='virtual')
# if neuron is sufficiently depolorized enough post synaptic calcium then synaptiic weight goes up
# pyr->pyr & pyr->PV
# PV->pyr PV->PV
def one_to_all(source, target):
sid = source.node_id
tid = target.node_id
print("connecting bio cell {} to bio cell {}".format(sid, tid))
return 1
def BG_to_PN_C(source, target):
sid = source.node_id
tid = target.node_id
if sid == tid:
print("connecting BG {} to PN_C{}".format(sid, tid))
return 1
else:
return 0
def BG_to_PV(source, target):
sid = source.node_id
tid = target.node_id
sid = sid + 1
if sid == tid:
print("connecting BG {} to PV{}".format(sid, tid))
return 1
else:
return 0
conn = net.add_edges(source=net.nodes(pop_name='PyrC'),
target=net.nodes(pop_name="PV"),
connection_rule=one_to_all,
syn_weight=1.0,
delay=0.1,
distance_range=[-10000, 10000],
dynamics_params='PN2PV.json',
model_template=syn['PN2PV.json']['level_of_detail'])
conn.add_properties(['sec_id', 'sec_x'],
rule=(1, 0.9),
dtypes=[np.int32, np.float])
conn = net.add_edges(source=net.nodes(pop_name='PV'),
target=net.nodes(pop_name="PyrC"),
connection_rule=one_to_all,
syn_weight=1.0,
delay=0.1,
distance_range=[-10000, 10000],
dynamics_params='PV2PN.json',
model_template=syn['PV2PN.json']['level_of_detail'])
conn.add_properties(['sec_id', 'sec_x'],
rule=(1, 0.9),
dtypes=[np.int32, np.float])
conn = net.add_edges(source=backgroundPN_C.nodes(),
target=net.nodes(pop_name='PyrC'),
connection_rule=BG_to_PN_C,
syn_weight=1.0,
delay=0.1,
distance_range=[-10000, 10000],
dynamics_params='BG2PNC.json',
model_template=syn['BG2PNC.json']['level_of_detail'])
conn.add_properties(['sec_id', 'sec_x'],
rule=(2, 0.9),
dtypes=[np.int32,
np.float]) # places syn on apic at 0.9
conn = net.add_edges(source=backgroundPV.nodes(),
target=net.nodes(pop_name='PV'),
connection_rule=BG_to_PV,
syn_weight=1.0,
delay=0.1,
distance_range=[-10000, 10000],
dynamics_params='BG2PV.json',
model_template=syn['BG2PV.json']['level_of_detail'])
conn.add_properties(['sec_id', 'sec_x'],
rule=(1, 0.9),
dtypes=[np.int32, np.float])
backgroundPN_C.build()
backgroundPN_C.save_nodes(output_dir='network')
backgroundPV.build()
backgroundPV.save_nodes(output_dir='network')
net.build()
net.save(output_dir='network')
# SPIKE TRAINS
t_sim = 40000
# build_env_bionet(base_dir='./',
# network_dir='./network',
# tstop=t_sim, dt=0.1,
# report_vars=['v'],
# components_dir='biophys_components',
# config_file='config.json',
# spikes_inputs=[('bg_pn_c', '2_cell_inputs/bg_pn_c_spikes.h5'),
# ('bg_pv', '2_cell_inputs/bg_pv_spikes.h5')],
# compile_mechanisms=False)
psg = PoissonSpikeGenerator(population='bg_pn_c')
psg.add(
node_ids=range(1), # need same number as cells
firing_rate=6, # 1 spike every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
psg.to_sonata('2_cell_inputs/bg_pn_c_spikes.h5')
print('Number of background spikes for PN_C: {}'.format(psg.n_spikes()))
psg = PoissonSpikeGenerator(population='bg_pv')
psg.add(
node_ids=range(1), # need same number as cells
firing_rate=7.7, # 8 spikes every 1 second Hz
times=(0.0, t_sim / 1000)) # time is in seconds for some reason
psg.to_sonata('2_cell_inputs/bg_pv_spikes.h5')
print('Number of background spikes for PV: {}'.format(psg.n_spikes()))