def step_math(self, dt, J, spiked, cells, voltage):
# 1. Determine voltage changes
dV = (dt / self.tau_rc) * J
spiketimes = np.array(
[c.spiketime if not c.refractory else 0.0 for c in cells])
dV += spiketimes * J / nrn_duration(self.tau_rc)
# 2. Apply voltage changes
for c, w in zip(cells, dV):
if not c.refractory:
c.spiketime = 0.0
c.in_con.weight[0] = w
c.in_con.event(neuron.h.t + nrn_duration(dt) / 2.0)
# 3. Setup recording of spikes
spikes = self._setup_spike_recorder(cells)
# 4. Simulate for one time step
neuron.run(neuron.h.t + nrn_duration(dt))
# 5. Check for spikes and record voltages
spiked[:] = [s.size() > 0 for s in spikes]
spiked /= dt
voltage[:] = [np.clip(c.neuron.M(), 0, 1) for c in cells]
# 6. Record spike times
for idx in np.where(spiked)[0]:
cells[idx].spiketime = neuron.h.t - spikes[idx][0]
cells[idx].neuron.refrac = nrn_duration(self.tau_ref +
dt) - cells[idx].spiketime
def __init__(self, tau_rc, tau_ref):
self.neuron = neuron.h.IntFire1()
self.neuron.tau = nrn_duration(tau_rc)
self.neuron.refrac = nrn_duration(tau_ref)
self.in_con = neuron.h.NetCon(None, self.neuron)
self.out_con = neuron.h.NetCon(self.neuron, None)
self.spiketime = 0.0
def step_math(self, dt, J, spiked, cells, voltage):
# 1. Determine voltage changes
dV = (dt / self.tau_rc) * J
spiketimes = np.array(
[c.spiketime if not c.refractory else 0.0 for c in cells])
dV += spiketimes * J / nrn_duration(self.tau_rc)
# 2. Apply voltage changes
for c, w in zip(cells, dV):
if not c.refractory:
c.spiketime = 0.0
c.in_con.weight[0] = w
c.in_con.event(neuron.h.t + nrn_duration(dt) / 2.0)
# 3. Setup recording of spikes
spikes = self._setup_spike_recorder(cells)
# 4. Simulate for one time step
neuron.run(neuron.h.t + nrn_duration(dt))
# 5. Check for spikes and record voltages
spiked[:] = [s.size() > 0 for s in spikes]
spiked /= dt
voltage[:] = [np.clip(c.neuron.M(), 0, 1) for c in cells]
# 6. Record spike times
for idx in np.where(spiked)[0]:
cells[idx].spiketime = neuron.h.t - spikes[idx][0]
cells[idx].neuron.refrac = nrn_duration(
self.tau_ref + dt) - cells[idx].spiketime
def __init__(self, tau_rc, tau_ref):
self.neuron = neuron.h.IntFire1()
self.neuron.tau = nrn_duration(tau_rc)
self.neuron.refrac = nrn_duration(tau_ref)
self.in_con = neuron.h.NetCon(None, self.neuron)
self.out_con = neuron.h.NetCon(self.neuron, None)
self.spiketime = 0.0
def create(self, sec, weight):
syn = neuron.h.FixedCurrent(sec)
syn.tau = nrn_duration(self.tau)
in_con = neuron.h.NetCon(None, syn)
in_con.weight[0] = weight
return self.SynapticCon(syn=syn, in_con=in_con)
def create(self, sec, weight):
syn = neuron.h.FixedCurrent(sec)
syn.tau = nrn_duration(self.tau)
in_con = neuron.h.NetCon(None, syn)
in_con.weight[0] = weight
return self.SynapticCon(syn=syn, in_con=in_con)
def step_math(self, dt, J, spiked, cells, voltage):
for c in cells:
c.spikes.resize(0)
# 1. Simulate for one time step
neuron.run(neuron.h.t + nrn_duration(dt))
# 2. Check for spikes
spiked[:] = [c.spikes.size() > 0 for c in cells]
spiked /= dt
voltage[:] = [c.neuron.soma.v for c in cells]
def step_math(self, dt, J, spiked, cells, voltage):
for c in cells:
c.spikes.resize(0)
# 1. Simulate for one time step
neuron.run(neuron.h.t + nrn_duration(dt))
# 2. Check for spikes
spiked[:] = [c.spikes.size() > 0 for c in cells]
spiked /= dt
voltage[:] = [c.neuron.soma.v for c in cells]
def create(self, sec, weight):
syn = neuron.h.ExpSyn(sec)
syn.tau = nrn_duration(self.tau)
if weight >= 0.0:
syn.e = self.e_exc
else:
syn.e = self.e_inh
in_con = neuron.h.NetCon(None, syn)
in_con.weight[0] = abs(weight)
return self.SynapticCon(syn=syn, in_con=in_con)
def create(self, sec, weight):
syn = neuron.h.ExpSyn(sec)
syn.tau = nrn_duration(self.tau)
if weight >= 0.0:
syn.e = self.e_exc
else:
syn.e = self.e_inh
in_con = neuron.h.NetCon(None, syn)
in_con.weight[0] = abs(weight)
return self.SynapticCon(syn=syn, in_con=in_con)