def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma, constraints=None,
label=None, tau_m=default_parameters['tau_m'],
cm=default_parameters['cm'],
v_rest=default_parameters['v_rest'],
v_reset=default_parameters['v_reset'],
v_thresh=default_parameters['v_thresh'],
tau_syn_E=default_parameters['tau_syn_E'],
tau_syn_E2=default_parameters['tau_syn_E2'],
tau_syn_I=default_parameters['tau_syn_I'],
tau_refrac=default_parameters['tau_refrac'],
i_offset=default_parameters['i_offset'],
v_init=None):
# Instantiate the parent classes
AbstractDualExponentialVertex.__init__(
self, n_neurons=n_neurons, tau_syn_E=tau_syn_E,
tau_syn_E2=tau_syn_E2, tau_syn_I=tau_syn_I,
machine_time_step=machine_time_step)
AbstractIntegrateAndFireProperties.__init__(
self, atoms=n_neurons, cm=cm, tau_m=tau_m, i_offset=i_offset,
v_init=v_init, v_reset=v_reset, v_rest=v_rest, v_thresh=v_thresh,
tau_refrac=tau_refrac)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, n_params=10, n_global_params=0,
label=label,
binary="IF_curr_exp_dual.aplx", constraints=constraints,
max_atoms_per_core=(IFCurrentDualExponentialPopulation
._model_based_max_atoms_per_core),
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)
def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma, constraints=None,
label=None, tau_m=20.0, cm=1.0, v_rest=-65.0, v_reset=-65.0,
v_thresh=-50.0, tau_syn_E=5.0, tau_syn_E2=5.0, tau_syn_I=5.0,
tau_refrac=0.1, i_offset=0, v_init=None):
# Instantiate the parent classes
AbstractDualExponentialVertex.__init__(
self, n_neurons=n_neurons, tau_syn_E=tau_syn_E,
tau_syn_E2=tau_syn_E2, tau_syn_I=tau_syn_I,
machine_time_step=machine_time_step)
AbstractIntegrateAndFireProperties.__init__(
self, atoms=n_neurons, cm=cm, tau_m=tau_m, i_offset=i_offset,
v_init=v_init, v_reset=v_reset, v_rest=v_rest, v_thresh=v_thresh,
tau_refrac=tau_refrac)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, n_params=10, label=label,
binary="IF_curr_exp_dual.aplx", constraints=constraints,
max_atoms_per_core=(IFCurrentDualExponentialPopulation
._model_based_max_atoms_per_core),
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)
self._executable_constant = \
IFCurrentDualExponentialPopulation.CORE_APP_IDENTIFIER
def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma, constraints=None,
label=None, a=0.02, c=-65.0, b=0.2, d=2.0, i_offset=0,
u_init=-14.0, v_init=-70.0, tau_syn_E=5.0, tau_syn_I=5.0,
membrane_noise_sd=2.5):
# Instantiate the parent classes
AbstractExponentialPopulationVertex.__init__(
self, n_neurons=n_neurons, tau_syn_E=tau_syn_E,
tau_syn_I=tau_syn_I, machine_time_step=machine_time_step)
AbstractIzhikevichVertex.__init__(self, n_neurons, a=a, c=c, b=b, d=d,
i_offset=i_offset, u_init=u_init,
v_init=v_init)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, n_params=11, label=label,
binary="izk_curr_exp_stochastic.aplx", constraints=constraints,
max_atoms_per_core=IzhikevichCurrentExponentialPopulation.
_model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)
AbstractStochasticVertex.__init__(self, membrane_noise_sd)
self._executable_constant = \
IzhikevichCurrentExponentialPopulation.CORE_APP_IDENTIFIER
def __init__(self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
constraints=None,
label=None):
# Instantiate the parent classes
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
n_params=10,
label=label,
binary="IZK_curr_exp.aplx",
constraints=constraints,
max_atoms_per_core=IzhikevichCurrentExponentialPopulation.
_model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)
self._executable_constant = \
IzhikevichCurrentExponentialPopulation.CORE_APP_IDENTIFIER
def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma, constraints=None,
label=None, tau_m=20, cm=1.0, e_rev_E=0.0, e_rev_I=-70.0,
v_rest=-65.0, v_reset=-65.0, v_thresh=-50.0, tau_syn_E=5.0,
tau_syn_I=5.0, tau_refrac=0.1, i_offset=0, v_init=None):
# Instantiate the parent classes
AbstractConductanceVertex.__init__(self, n_neurons, e_rev_E=e_rev_E,
e_rev_I=e_rev_I)
AbstractExponentialPopulationVertex.__init__(
self, n_neurons=n_neurons, tau_syn_E=tau_syn_E,
tau_syn_I=tau_syn_I, machine_time_step=machine_time_step)
AbstractIntegrateAndFireProperties.__init__(
self, atoms=n_neurons, cm=cm, tau_m=tau_m, i_offset=i_offset,
v_init=v_init, v_reset=v_reset, v_rest=v_rest, v_thresh=v_thresh,
tau_refrac=tau_refrac)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, n_params=12, label=label,
max_atoms_per_core=(IFConductanceExponentialPopulation
._model_based_max_atoms_per_core),
binary="IF_cond_exp.aplx", constraints=constraints,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma,
weight_scale=AbstractConductanceVertex.WEIGHT_SCALE)
def __init__(self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
constraints=None,
label=None,
tau_m=20.0,
cm=1.0,
v_rest=-65.0,
v_reset=-65.0,
v_thresh=-50.0,
tau_syn_E=5.0,
tau_syn_E2=5.0,
tau_syn_I=5.0,
tau_refrac=0.1,
i_offset=0,
v_init=None):
# Instantiate the parent classes
AbstractDualExponentialVertex.__init__(
self,
n_neurons=n_neurons,
tau_syn_E=tau_syn_E,
tau_syn_E2=tau_syn_E2,
tau_syn_I=tau_syn_I,
machine_time_step=machine_time_step)
AbstractIntegrateAndFireProperties.__init__(self,
atoms=n_neurons,
cm=cm,
tau_m=tau_m,
i_offset=i_offset,
v_init=v_init,
v_reset=v_reset,
v_rest=v_rest,
v_thresh=v_thresh,
tau_refrac=tau_refrac)
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
n_params=10,
label=label,
binary="IF_curr_exp_dual.aplx",
constraints=constraints,
max_atoms_per_core=(IFCurrentDualExponentialPopulation.
_model_based_max_atoms_per_core),
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)
def __init__(self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
constraints=None,
label=None,
a=0.02,
c=-65.0,
b=0.2,
d=2.0,
i_offset=0,
u_init=-14.0,
v_init=-70.0,
tau_syn_E=5.0,
tau_syn_I=5.0):
# Instantiate the parent classes
AbstractExponentialPopulationVertex.__init__(
self,
n_neurons=n_neurons,
tau_syn_E=tau_syn_E,
tau_syn_I=tau_syn_I,
machine_time_step=machine_time_step)
AbstractIzhikevichVertex.__init__(self,
n_neurons,
a=a,
c=c,
b=b,
d=d,
i_offset=i_offset,
u_init=u_init,
v_init=v_init)
AbstractPopulationVertex.__init__(
self,
n_neurons=n_neurons,
n_params=10,
label=label,
binary="IZK_curr_exp.aplx",
constraints=constraints,
max_atoms_per_core=IzhikevichCurrentExponentialPopulation.
_model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)
def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma, constraints=None,
label=None, a=0.02, c=-65.0, b=0.2, d=2.0, i_offset=0,
u_init=-14.0, v_init=-70.0, tau_syn_E=5.0, tau_syn_I=5.0):
# Instantiate the parent classes
AbstractExponentialPopulationVertex.__init__(
self, n_neurons=n_neurons, tau_syn_E=tau_syn_E,
tau_syn_I=tau_syn_I, machine_time_step=machine_time_step)
AbstractIzhikevichVertex.__init__(self, n_neurons, a=a, c=c, b=b, d=d,
i_offset=i_offset, u_init=u_init,
v_init=v_init)
AbstractPopulationVertex.__init__(
self, n_neurons=n_neurons, n_params=10, label=label,
binary="IZK_curr_exp.aplx", constraints=constraints,
max_atoms_per_core=IzhikevichCurrentExponentialPopulation.
_model_based_max_atoms_per_core,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
spikes_per_second=spikes_per_second,
ring_buffer_sigma=ring_buffer_sigma)