def get_neural_parameters(self):
return [
# membrane voltage [mV]
# REAL V_membrane;
NeuronParameter(self._v_init, DataType.S1615),
# membrane resting voltage [mV]
# REAL V_rest;
NeuronParameter(self._v_rest, DataType.S1615),
# membrane resistance [MOhm]
# REAL R_membrane;
NeuronParameter(self._r_membrane, DataType.S1615),
# 'fixed' computation parameter - time constant multiplier for
# closed-form solution
# exp( -(machine time step in ms)/(R * C) ) [.]
# REAL exp_TC;
NeuronParameter(self._exp_tc, DataType.S1615),
# offset current [nA]
# REAL I_offset;
NeuronParameter(self._i_offset, DataType.S1615)
]
def get_neural_parameters(self):
# Note: this must match the order of the parameters in the neuron_t
# data structure in the C code
return [
NeuronParameter(-1, DataType.INT32),
NeuronParameter(self._primary, DataType.INT32)
]
def get_neural_parameters(self, machine_time_step):
return [
# membrane voltage [mV]
# REAL V_membrane;
NeuronParameter(self._v_init, _IF_TYPES.V_INIT.data_type),
# membrane resting voltage [mV]
# REAL V_rest;
NeuronParameter(self._v_rest, _IF_TYPES.V_REST.data_type),
# membrane resistance [MOhm]
# REAL R_membrane;
NeuronParameter(self._r_membrane, _IF_TYPES.R_MEMBRANE.data_type),
# 'fixed' computation parameter - time constant multiplier for
# closed-form solution
# exp( -(machine time step in ms)/(R * C) ) [.]
# REAL exp_TC;
NeuronParameter(self._exp_tc(machine_time_step),
_IF_TYPES.EXP_TC.data_type),
# offset current [nA]
# REAL I_offset;
NeuronParameter(self._i_offset, _IF_TYPES.I_OFFSET.data_type)
]
def get_input_type_parameters(self):
return [
NeuronParameter(self._e_rev_E,
_CONDUCTANTCE_TYPES.E_REV_E.data_type),
NeuronParameter(self._e_rev_I,
_CONDUCTANTCE_TYPES.E_REV_I.data_type)
]
def get_synapse_type_parameters(self):
# TODO: update to return the parameters
# Note: The order of the parameters must match the order in the
# synapse_param_t data structure in the C code
return [
NeuronParameter(self._my_ex_synapse_parameter, DataType.S1615),
NeuronParameter(self._my_in_synapse_parameter, DataType.S1615),
]
def get_threshold_parameters(self):
# TODO: update to the return the parameters
# Note: The order of the parameters must match the order in the
# threshold_type_t data structure in the C code
return [
NeuronParameter(self._threshold_value, DataType.S1615),
NeuronParameter(self._my_threshold_parameter, DataType.S1615)
]
def get_synapse_type_parameters(self):
d_decay, d_init = get_exponential_decay_and_init(
self._a_syn_D, self._tau_syn_D, self._machine_time_step)
s_decay, s_init = get_exponential_decay_and_init(
self._a_syn_S, self._tau_syn_S, self._machine_time_step)
return [
NeuronParameter(d_decay, DataType.S1615),
NeuronParameter(d_init, DataType.S1615),
NeuronParameter(s_decay, DataType.S1615),
NeuronParameter(s_init, DataType.S1615)
]
def get_synapse_type_parameters(self):
e_decay, e_init = get_exponential_decay_and_init(
self._tau_syn_E, self._machine_time_step)
i_decay, i_init = get_exponential_decay_and_init(
self._tau_syn_I, self._machine_time_step)
return [
NeuronParameter(e_decay, DataType.UINT32),
NeuronParameter(e_init, DataType.UINT32),
NeuronParameter(i_decay, DataType.UINT32),
NeuronParameter(i_init, DataType.UINT32)
]
def get_neural_parameters(self):
return [
# REAL A
NeuronParameter(self._a, DataType.S1615),
# REAL B
NeuronParameter(self._b, DataType.S1615),
# REAL C
NeuronParameter(self._c, DataType.S1615),
# REAL D
NeuronParameter(self._d, DataType.S1615),
# REAL V
NeuronParameter(self._v_init, DataType.S1615),
# REAL U
NeuronParameter(self._u_init, DataType.S1615),
# offset current [nA]
# REAL I_offset;
NeuronParameter(self._i_offset, DataType.S1615),
# current timestep - simple correction for threshold
# REAL this_h;
NeuronParameter(self._machine_time_step / 1000.0, DataType.S1615)
]
def get_parameters(self):
"""
Generate Neuron Parameter data (region 2):
"""
# Get the parameters:
# typedef struct neuron_t {
#
# // nominally 'fixed' parameters
# REAL A;
# REAL B;
# REAL C;
# REAL D;
#
# // Variable-state parameters
# REAL V;
# REAL U;
#
# // offset current [nA]
# REAL I_offset;
#
# // current timestep - simple correction for threshold in beta version
# REAL this_h;
# } neuron_t;
return [
NeuronParameter(self._a, DataType.S1615),
NeuronParameter(self._b, DataType.S1615),
NeuronParameter(self._c, DataType.S1615),
NeuronParameter(self._d, DataType.S1615),
NeuronParameter(self._v_init, DataType.S1615),
NeuronParameter(self._u_init, DataType.S1615),
NeuronParameter(self.ioffset, DataType.S1615),
NeuronParameter(self._machine_time_step / 1000.0, DataType.S1615)
]
def get_neural_parameters(self, machine_time_step):
return [
# REAL A
NeuronParameter(self._a, _IZH_TYPES.A.data_type),
# REAL B
NeuronParameter(self._b, _IZH_TYPES.B.data_type),
# REAL C
NeuronParameter(self._c, _IZH_TYPES.C.data_type),
# REAL D
NeuronParameter(self._d, _IZH_TYPES.D.data_type),
# REAL V
NeuronParameter(self._v_init, _IZH_TYPES.V_INIT.data_type),
# REAL U
NeuronParameter(self._u_init, _IZH_TYPES.U_INIT.data_type),
# offset current [nA]
# REAL I_offset;
NeuronParameter(self._i_offset, _IZH_TYPES.I_OFFSET.data_type),
# current timestep - simple correction for threshold
# REAL this_h;
NeuronParameter(machine_time_step / 1000.0,
_IZH_TYPES.THIS_H.data_type)
]
def get_synapse_type_parameters(self, machine_time_step):
# pylint: disable=arguments-differ
e_decay, _ = get_exponential_decay_and_init(
self._data[TAU_SYN_E], machine_time_step)
i_decay, _ = get_exponential_decay_and_init(
self._data[TAU_SYN_I], machine_time_step)
# pre-multiply constants (convert to millisecond)
dt_divided_by_tau_syn_E_sqr = self._data[TAU_SYN_E].apply_operation(
lambda x: (float(machine_time_step) / 1000.0) / (x * x))
dt_divided_by_tau_syn_I_sqr = self._data[TAU_SYN_I].apply_operation(
lambda x: (float(machine_time_step) / 1000.0) / (x * x))
return [
# linear term buffer
NeuronParameter(self._data[EXC_RESPONSE],
_COMB_EXP_TYPES.RESPONSE_EXC.data_type),
# exponential term buffer
NeuronParameter(self._data[EXC_EXP_RESPONSE],
_COMB_EXP_TYPES.RESPONSE_EXC_EXP.data_type),
# evolution parameters
NeuronParameter(dt_divided_by_tau_syn_E_sqr,
_COMB_EXP_TYPES.CONST_EXC.data_type),
NeuronParameter(e_decay, _COMB_EXP_TYPES.DECAY_EXC.data_type),
NeuronParameter(self._data[INH_RESPONSE],
_COMB_EXP_TYPES.RESPONSE_INH.data_type),
NeuronParameter(self._data[INH_EXP_RESPONSE],
_COMB_EXP_TYPES.RESPONSE_INH_EXP.data_type),
NeuronParameter(dt_divided_by_tau_syn_I_sqr,
_COMB_EXP_TYPES.CONST_INH.data_type),
NeuronParameter(i_decay, _COMB_EXP_TYPES.DECAY_INH.data_type),
]
def get_parameters(self):
""" Get the parameters for the additional input
:return: An array of parameters
:rtype: array of\
:py:class:`spynnaker.pyNN.models.neural_properties.neural_parameter.NeuronParameter`
"""
# TODO: update the parameters
# Note: must match the order of the additional_input_t structure in
# the C code
return [
NeuronParameter(0, DataType.S1615),
NeuronParameter(self._my_additional_input_parameter,
DataType.S1615),
]
def get_neural_parameters(self):
# TODO: update to match the parameters and state variables
# Note: this must match the order of the parameters in the neuron_t
# data structure in the C code
return [
# REAL V;
NeuronParameter(self._v_init, DataType.S1615),
# REAL I_offset;
NeuronParameter(self._i_offset, DataType.S1615),
# REAL my_parameter;
NeuronParameter(self._my_neuron_parameter, DataType.S1615)
]
def get_neural_parameters(self):
params = NeuronModelLeakyIntegrate.get_neural_parameters(self)
params.extend([
# countdown to end of next refractory period [timesteps]
# int32_t refract_timer;
NeuronParameter(0, DataType.INT32),
# post-spike reset membrane voltage [mV]
# REAL V_reset;
NeuronParameter(self._v_reset, DataType.S1615),
# refractory time of neuron [timesteps]
# int32_t T_refract;
NeuronParameter(self._tau_refrac_timesteps, DataType.INT32)
])
return params
def get_parameters(self):
"""
Generate Neuron Parameter data (region 2):
"""
# Get the parameters
return [
# membrane voltage threshold at which neuron spikes [mV]
# REAL V_thresh;
NeuronParameter(self._v_thresh, DataType.S1615),
# post-spike reset membrane voltage [mV]
# REAL V_reset;
NeuronParameter(self._v_reset, DataType.S1615),
# membrane resting voltage [mV]
# REAL V_rest;
NeuronParameter(self._v_rest, DataType.S1615),
# membrane resistance
# REAL R_membrane;
NeuronParameter(self._r_membrane, DataType.S1615),
# membrane voltage [mV]
# REAL V_membrane;
NeuronParameter(self._v_init, DataType.S1615),
# offset current [nA]
# REAL I_offset;
NeuronParameter(self.ioffset, DataType.S1615),
# 'fixed' computation parameter - time constant multiplier for
# closed-form solution
# exp( -(machine time step in ms)/(R * C) ) [.]
# REAL exp_TC;
NeuronParameter(self._exp_tc(self._machine_time_step),
DataType.S1615),
# countdown to end of next refractory period [timesteps]
# int32_t refract_timer;
NeuronParameter(self._refract_timer, DataType.INT32),
# refractory time of neuron [timesteps]
# int32_t T_refract;
NeuronParameter(
self._tau_refract_timesteps(self._machine_time_step),
DataType.INT32)
]
def get_neural_parameters(self, machine_time_step):
params = NeuronModelLeakyIntegrate.get_neural_parameters(self)
params.extend([
# count down to end of next refractory period [timesteps]
# int32_t refract_timer;
NeuronParameter(self._countdown_to_refactory_period,
_LIF_TYPES.REFRACT_COUNT.data_type),
# post-spike reset membrane voltage [mV]
# REAL V_reset;
NeuronParameter(self._v_reset, _LIF_TYPES.V_RESET.data_type),
# refractory time of neuron [timesteps]
# int32_t T_refract;
NeuronParameter(self._tau_refrac_timesteps(machine_time_step),
_LIF_TYPES.TAU_REFRACT.data_type)
])
return params
def get_global_parameters(self, machine_time_step):
# TODO: update to match the global parameters
# Note: This must match the order of the parameters in the
# global_neuron_t data structure in the C code
return [
# uint32_t machine_time_step
NeuronParameter(machine_time_step, DataType.UINT32)
]
def get_global_parameters(self):
# Note: This must match the order of the parameters in the
# global_neuron_t data structure in the C code
return [
# uint32_t machine_time_step
NeuronParameter(self._machine_time_step, DataType.UINT32),
NeuronParameter(0.0, DataType.S1615),
NeuronParameter(0.0, DataType.S1615),
NeuronParameter(0, DataType.INT32),
NeuronParameter(0.0, DataType.S1615),
NeuronParameter(0, DataType.INT32),
NeuronParameter(0.0, DataType.S1615),
NeuronParameter(0.0, DataType.S1615),
NeuronParameter(0.0, DataType.S1615)
]
def get_synapse_type_parameters(self, machine_time_step):
e_decay, e_init = get_exponential_decay_and_init(
self._tau_syn_E, machine_time_step)
e_decay2, e_init2 = get_exponential_decay_and_init(
self._tau_syn_E2, machine_time_step)
i_decay, i_init = get_exponential_decay_and_init(
self._tau_syn_I, machine_time_step)
return [
NeuronParameter(e_decay, _DUAL_EXP_TYPES.E_DECAY.data_type),
NeuronParameter(e_init, _DUAL_EXP_TYPES.E_INIT.data_type),
NeuronParameter(e_decay2, _DUAL_EXP_TYPES.E2_DECAY.data_type),
NeuronParameter(e_init2, _DUAL_EXP_TYPES.E2_INIT.data_type),
NeuronParameter(i_decay, _DUAL_EXP_TYPES.I_DECAY.data_type),
NeuronParameter(i_init, _DUAL_EXP_TYPES.I_INIT.data_type),
NeuronParameter(self._initial_input_exc,
_DUAL_EXP_TYPES.INITIAL_EXC.data_type),
NeuronParameter(self._initial_input_exc2,
_DUAL_EXP_TYPES.INITIAL_EXC2.data_type),
NeuronParameter(self._initial_input_inh,
_DUAL_EXP_TYPES.INITIAL_INH.data_type)
]
def get_parameters(self):
"""
Generate Neuron Parameter data (region 2):
"""
# Get the parameters:
# typedef struct neuron_t {
#
# // nominally 'fixed' parameters
# REAL A;
# REAL B;
# REAL C;
# REAL D;
#
# // Variable-state parameters
# REAL V;
# REAL U;
#
# // offset current [nA]
# REAL I_offset;
#
# // current timestep - simple correction for threshold in beta version
# REAL this_h;
# } neuron_t;
return [
# Currently softfloat not defined on the Python side, so have to use S1615
NeuronParameter(self._a, DataType.S1615),
NeuronParameter(self._b, DataType.S1615),
NeuronParameter(self._c, DataType.S1615),
NeuronParameter(self._d, DataType.S1615),
NeuronParameter(self._v_init, DataType.S1615),
NeuronParameter(self._u_init, DataType.S1615),
# delete this
NeuronParameter(self.ioffset(self._machine_time_step),
DataType.S1615),
#NeuronParameter(0, DataType.S1615) # no idea what this, delete it
]
def get_threshold_parameters(self):
return [NeuronParameter(self._v_thresh, DataType.S1615)]
def get_global_parameters(self, machine_time_step):
return [
NeuronParameter(machine_time_step / 1000.0,
_IZH_GLOBAL_TYPES.TIMESTEP.data_type)
]
def get_threshold_parameters(self):
return [
NeuronParameter(self._v_thresh, _STATIC_TYPES.V_THRESH.data_type)
]
def get_global_parameters(self):
return [
NeuronParameter(self._machine_time_step / 1000.0, DataType.S1615)
]
def get_input_type_parameters(self):
return [
NeuronParameter(self._e_rev_E, DataType.S1615),
NeuronParameter(self._e_rev_I, DataType.S1615)
]
def test_create_new_neuron_parameter_none_datatype(self):
np = NeuronParameter(1, None)
self.assertEqual(np.get_value(), 1)
self.assertEqual(np.get_dataspec_datatype(), None)
def get_parameters(self):
return [
NeuronParameter(self._exp_tau_ca2, DataType.S1615),
NeuronParameter(self._i_ca2, DataType.S1615),
NeuronParameter(self._i_alpha, DataType.S1615)
]
