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
psg = PoissonSpikeGenerator()
psg.add(node_ids=[0],
firing_rate=firing_rate,
times=(next_block_tstart, next_block_tstop))
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)
# 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()
psg.add(node_ids=[0],
firing_rate=150,
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)
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=0.0)
psg.add(node_ids=range(10), firing_rate=10.0, times=(0.0, 10.0))
assert(psg.populations == ['test'])
assert(psg.nodes() == list(range(10)))
time_range = psg.time_range()
assert(0 <= time_range[0] < 1.0)
assert(9.0 < time_range[1] <= 10.0)
# This may fail on certain versions
assert(psg.get_times(node_id=5).size > 10)
assert(0 < psg.get_times(node_id=8).size < 300)
for i in range(10):
spikes = psg.get_times(i)
assert(np.max(spikes) > 0.1)
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))
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, 10.0, 1000)
fr = np.exp(-np.power(times - 5.0, 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(psg.nodes() == list(range(10)))
for i in range(10):
spikes = psg.get_times(i)
assert(len(spikes) > 0)
assert(1.0 < np.min(spikes))
assert(np.max(spikes) < 9.0)
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
psg = PoissonSpikeGenerator()
psg.add(node_ids=[0], firing_rate=firing_rate/1000, times=(next_block_tstart, next_block_tstop))
self._spike_events = psg.get_times(0)
if self._spike_events.shape[0]==0:
self._spike_events = np.array((next_block_tstart+0.2,))
# 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)
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
# TODO: See if we've reached the end of the simulation
# 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. For this case I'm just setting the high-level neuron to start bursting
#if firing_rate > firing_rate_threshold:
#self._spike_events = np.linspace(next_block_tstart, next_block_tstop, 500)
psg = PoissonSpikeGenerator()
psg.add(node_ids=[0],
firing_rate=firing_rate,
times=(next_block_tstart, next_block_tstop))
self._spike_events = psg.get_times(0)
for gid in self._high_level_neurons:
nc = self._netcons[gid]
for t in self._spike_events:
nc.event(t)
def _activate_hln(self, sim, block_interval, firing_rate):
block_length = sim.nsteps_block * sim.dt / 1000.0
next_block_tstart = (block_interval[1] +
1) * sim.dt / 1000.0 # The next time-step
next_block_tstop = next_block_tstart + block_length # 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()
# # Use homogeneous input
# psg.add(node_ids=[0], firing_rate=firing_rate, times=(next_block_tstart, next_block_tstop)) # sec
# spikes = psg.get_times([0])*1000 # convert sec to ms
# n_spikes = len(spikes)
# io.log_info(' _activate_hln firing rate: {:.2f} Hz'.format(n_spikes/block_length))
# if n_spikes > 0:
# # Update firing rate of bladder afferent neurons
# for gid in self._high_level_neurons:
# self._spike_events[gid] = np.concatenate((self._spike_events[gid],spikes))
# nc = self._netcons[gid]
# for t in spikes:
# nc.event(t)
# io.log_info('Last spike: {:.1f} ms'.format(spikes[-1]))
# Use inhomogeneous input
n = len(self._high_level_neurons)
psg.add(node_ids=self._high_level_neurons,
firing_rate=firing_rate,
times=(next_block_tstart, next_block_tstop))
n_spikes = np.zeros(n)
last_spike = 0.0
for i, gid in enumerate(self._high_level_neurons):
spikes = psg.get_times(gid) * 1000
n_spikes[i] = len(spikes)
if n_spikes[i] > 0:
self._spike_events[gid] = np.concatenate(
(self._spike_events[gid], spikes))
nc = self._netcons[gid]
for t in spikes:
nc.event(t)
last_spike = max(last_spike, spikes[-1])
io.log_info(
' _activate_hln firing rate: ' +
','.join(["%.2f" % (ns / block_length)
for ns in n_spikes]) + ' Hz')
if last_spike > 0:
io.log_info('Last spike: {:.1f} ms'.format(last_spike))
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)
################ Activate higher order based on afferent firing rate ##############################
if firing_rate > 10:
pag_fr = 15
psg = PoissonSpikeGenerator()
# # Use homogeneous input
# psg.add(node_ids=[0], firing_rate=pag_fr, times=(next_block_tstart, next_block_tstop))
# spikes = psg.get_times([0])*1000
# n_spikes = len(spikes)
# io.log_info(' pag firing rate: {:.2f} Hz'.format(n_spikes/block_length))
# if n_spikes>0:
# io.log_info('Last spike: {:.1f} ms'.format(spikes[-1]))
# for gid in self._pag_neurons:
# self._spike_events[gid] = np.concatenate((self._spike_events[gid],spikes))
# nc = self._netcons[gid]
# for t in spikes:
# nc.event(t)
# Use inhomogeneous input
n = len(self._pag_neurons)
psg.add(node_ids=self._pag_neurons,
firing_rate=pag_fr,
times=(next_block_tstart, next_block_tstop))
n_spikes = np.zeros(n)
last_spike = 0.0
for i, gid in enumerate(self._pag_neurons):
spikes = psg.get_times(gid) * 1000
n_spikes[i] = len(spikes)
if n_spikes[i] > 0:
self._spike_events[gid] = np.concatenate(
(self._spike_events[gid], spikes))
nc = self._netcons[gid]
for t in spikes:
nc.event(t)
last_spike = max(last_spike, spikes[-1])
io.log_info(
' pag firing rate: ' +
','.join(["%.2f" % (ns / block_length)
for ns in n_spikes]) + ' Hz')
if last_spike > 0:
io.log_info('Last spike: {:.1f} ms'.format(last_spike))
io.log_info('\n')