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')