def __init__( self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, 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): neuron_model = NeuronModelLeakyIntegrateAndFire( n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = SynapseTypeDualExponential( n_neurons, machine_time_step, tau_syn_E, tau_syn_E2, tau_syn_I) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IF_curr_exp_dual.aplx", label=label, max_atoms_per_core=IFCurrDualExp._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, model_name="IF_curr_dual_exp", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, constraints=constraints)
def __init__( self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, constraints=None, label=None, a=default_parameters['a'], b=default_parameters['b'], c=default_parameters['c'], d=default_parameters['d'], i_offset=default_parameters['i_offset'], u_init=default_parameters['u_init'], v_init=default_parameters['v_init'], tau_syn_E=default_parameters['tau_syn_E'], tau_syn_I=default_parameters['tau_syn_I']): neuron_model = NeuronModelIzh( n_neurons, machine_time_step, a, b, c, d, v_init, u_init, i_offset) synapse_type = SynapseTypeExponential( n_neurons, machine_time_step, tau_syn_E, tau_syn_I) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IZK_curr_exp.aplx", label=label, max_atoms_per_core=IzkCurrExp._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, model_name="IZK_curr_exp", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, constraints=constraints)
def __init__( self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None, label=None, a=default_parameters['a'], b=default_parameters['b'], c=default_parameters['c'], d=default_parameters['d'], i_offset=default_parameters['i_offset'], u_init=default_parameters['u_init'], v_init=default_parameters['v_init'], tau_syn_E=default_parameters['tau_syn_E'], tau_syn_I=default_parameters['tau_syn_I'], e_rev_E=default_parameters['e_rev_E'], e_rev_I=default_parameters['e_rev_I']): neuron_model = NeuronModelIzh( n_neurons, machine_time_step, a, b, c, d, v_init, u_init, i_offset) synapse_type = SynapseTypeExponential( n_neurons, machine_time_step, tau_syn_E, tau_syn_I) input_type = InputTypeConductance(n_neurons, e_rev_E, e_rev_I) threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IZK_cond_exp.aplx", label=label, max_atoms_per_core=IzkCondExp._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, incoming_spike_buffer_size=incoming_spike_buffer_size, model_name="IZK_cond_exp", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, constraints=constraints)
def __init__( self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, 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_I=default_parameters['tau_syn_I'], tau_refrac=default_parameters['tau_refrac'], i_offset=default_parameters['i_offset'], e_rev_E=default_parameters['e_rev_E'], e_rev_I=default_parameters['e_rev_I'], v_init=None): neuron_model = NeuronModelLeakyIntegrateAndFire( n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = SynapseTypeExponential( n_neurons, machine_time_step, tau_syn_E, tau_syn_I) input_type = InputTypeConductance(n_neurons, e_rev_E, e_rev_I) threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IF_cond_exp.aplx", label=label, max_atoms_per_core=IFCondExp._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, incoming_spike_buffer_size=incoming_spike_buffer_size, model_name="IF_cond_exp", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, constraints=constraints)
def __init__(self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, 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_I=default_parameters['tau_syn_I'], tau_refrac=default_parameters['tau_refrac'], i_offset=default_parameters['i_offset'], v_init=None): neuron_model = NeuronModelLeakyIntegrateAndFire( n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = ExpSupervision(n_neurons, machine_time_step, tau_syn_E, tau_syn_I) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) # create additional inputs additional_input = None # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, machine_time_step=machine_time_step, timescale_factor=timescale_factor, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, max_atoms_per_core=( IFCurrExpSupervision._model_based_max_atoms_per_core), # the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # the model a name (shown in reports) model_name="IFCurrExpSupervision", # the matching binary name binary="if_curr_exp_supervision.aplx")
def __init__(self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, 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_I=default_parameters['tau_syn_I'], tau_refrac=default_parameters['tau_refrac'], i_offset=default_parameters['i_offset'], tau_ca2=default_parameters["tau_ca2"], i_ca2=default_parameters["i_ca2"], i_alpha=default_parameters["i_alpha"], v_init=None): neuron_model = NeuronModelLeakyIntegrateAndFire( n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = SynapseTypeExponential(n_neurons, machine_time_step, tau_syn_E, tau_syn_I) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) additional_input = AdditionalInputCa2Adaptive(n_neurons, machine_time_step, tau_ca2, i_ca2, i_alpha) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IF_curr_exp_ca2_adaptive.aplx", label=label, max_atoms_per_core=IFCurrExpCa2Adaptive. _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, incoming_spike_buffer_size=incoming_spike_buffer_size, model_name="IF_curr_exp_ca2_adaptive", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, constraints=constraints)
def __init__(self, n_neurons, spikes_per_second=AbstractPopulationVertex. none_pynn_default_parameters['spikes_per_second'], ring_buffer_sigma=AbstractPopulationVertex. none_pynn_default_parameters['ring_buffer_sigma'], incoming_spike_buffer_size=AbstractPopulationVertex. none_pynn_default_parameters['incoming_spike_buffer_size'], constraints=AbstractPopulationVertex. none_pynn_default_parameters['constraints'], label=AbstractPopulationVertex. none_pynn_default_parameters['label'], 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, isyn_exc=default_parameters['isyn_exc'], isyn_inh=default_parameters['isyn_inh'], isyn2_exc=default_parameters['isyn2_exc']): neuron_model = NeuronModelLeakyIntegrateAndFire( n_neurons, v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = SynapseTypeDualExponential(n_neurons, tau_syn_E, tau_syn_E2, tau_syn_I, isyn_exc, isyn2_exc, isyn_inh) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IF_curr_exp_dual.aplx", label=label, max_atoms_per_core=IFCurrDualExpBase. _model_based_max_atoms_per_core, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, model_name="IF_curr_dual_exp", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, constraints=constraints)
def __init__(self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, 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_I=default_parameters['tau_syn_I'], tau_refrac=default_parameters['tau_refrac'], i_offset=default_parameters['i_offset'], v_init=None): neuron_model = NeuronModelLeakyIntegrateAndFire( n_neurons, machine_time_step, v_init, v_rest, tau_m, cm, i_offset, v_reset, tau_refrac) synapse_type = ExpSupervision( n_neurons, machine_time_step, tau_syn_E, tau_syn_I) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) # create additional inputs additional_input = None # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, machine_time_step=machine_time_step, timescale_factor=timescale_factor, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, max_atoms_per_core=( IFCurrExpSupervision._model_based_max_atoms_per_core), # the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # the model a name (shown in reports) model_name="IFCurrExpSupervision", # the matching binary name binary="if_curr_exp_supervision.aplx")
def __init__(self, n_neurons, spikes_per_second=AbstractPopulationVertex. none_pynn_default_parameters['spikes_per_second'], ring_buffer_sigma=AbstractPopulationVertex. none_pynn_default_parameters['ring_buffer_sigma'], incoming_spike_buffer_size=AbstractPopulationVertex. none_pynn_default_parameters['incoming_spike_buffer_size'], constraints=AbstractPopulationVertex. none_pynn_default_parameters['constraints'], label=AbstractPopulationVertex. none_pynn_default_parameters['label'], a=default_parameters['a'], b=default_parameters['b'], c=default_parameters['c'], d=default_parameters['d'], i_offset=default_parameters['i_offset'], u_init=default_parameters['u_init'], v_init=default_parameters['v_init'], tau_syn_E=default_parameters['tau_syn_E'], tau_syn_I=default_parameters['tau_syn_I'], isyn_exc=default_parameters['isyn_exc'], isyn_inh=default_parameters['isyn_inh']): neuron_model = NeuronModelIzh(n_neurons, a, b, c, d, v_init, u_init, i_offset) synapse_type = SynapseTypeExponential(n_neurons, tau_syn_E, tau_syn_I, isyn_exc, isyn_inh) input_type = InputTypeCurrent() threshold_type = ThresholdTypeStatic(n_neurons, _IZK_THRESHOLD) AbstractPopulationVertex.__init__( self, n_neurons=n_neurons, binary="IZK_curr_exp.aplx", label=label, max_atoms_per_core=IzkCurrExpBase._model_based_max_atoms_per_core, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, model_name="IZK_curr_exp", neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, constraints=constraints)
def __init__( self, n_neurons, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None, label=None, # neuron model parameters my_parameter=default_parameters['my_parameter'], i_offset=default_parameters['i_offset'], # threshold types parameters v_thresh=default_parameters['v_thresh'], # synapse type parameters my_ex_synapse_parameter=default_parameters[ 'my_ex_synapse_parameter'], my_in_synapse_parameter=default_parameters[ 'my_in_synapse_parameter'], # state variables v_init=None): # create neuron model class neuron_model = MyNeuronModel(n_neurons, i_offset, my_parameter) # create synapse type model synapse_type = MySynapseType(n_neurons, my_ex_synapse_parameter, my_in_synapse_parameter) # create input type model input_type = InputTypeCurrent() # create threshold type model threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) # create additional inputs additional_input = None # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, max_atoms_per_core=( MyModelCurrMySynapseType._model_based_max_atoms_per_core), # the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # the model a name (shown in reports) model_name="MyModelMySynapseType", # the matching binary name binary="my_model_curr_my_synapse_type.aplx")
def __init__( self, n_neurons, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None, label=None, # neuron model parameters my_parameter=default_parameters['my_parameter'], i_offset=default_parameters['i_offset'], # threshold types parameters v_thresh=default_parameters['v_thresh'], # synapse type parameters tau_syn_E=default_parameters['tau_syn_E'], tau_syn_I=default_parameters['tau_syn_I'], # additional input parameter my_additional_input_parameter=( default_parameters['my_additional_input_parameter']), # state variables v_init=None): # create neuron model class neuron_model = MyNeuronModel( n_neurons, i_offset, my_parameter) # create synapse type model synapse_type = SynapseTypeExponential( n_neurons, tau_syn_E, tau_syn_I) # create input type model input_type = InputTypeCurrent() # create threshold type model threshold_type = ThresholdTypeStatic( n_neurons, v_thresh) # create additional inputs additional_input = MyAdditionalInput( n_neurons, my_additional_input_parameter) # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, max_atoms_per_core=( MyModelCurrExpMyAdditionalInput. _model_based_max_atoms_per_core), # the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # the model a name (shown in reports) model_name="MyModelCurrExpMyAdditionalInput", # the matching binary name binary="my_model_curr_exp_my_additional_input.aplx")
def __init__( self, n_neurons, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None, label=None, # TODO: neuron model parameters (add / remove as required) # neuron model parameters my_parameter=default_parameters['my_parameter'], i_offset=default_parameters['i_offset'], # TODO: threshold types parameters (add / remove as required) # threshold types parameters v_thresh=default_parameters['v_thresh'], # TODO: synapse type parameters (add /remove as required) # synapse type parameters tau_syn_E=default_parameters['tau_syn_E'], tau_syn_I=default_parameters['tau_syn_I'], # TODO: Optionally, you can add initial values for the state # variables; this is not technically done in PyNN v_init=None): # TODO: create your neuron model class (change if required) # create your neuron model class neuron_model = MyNeuronModel(n_neurons, i_offset, my_parameter) # TODO: create your synapse type model class (change if required) # create your synapse type model synapse_type = SynapseTypeExponential(n_neurons, tau_syn_E, tau_syn_I) # TODO: create your input type model class (change if required) # create your input type model input_type = InputTypeCurrent() # TODO: create your threshold type model class (change if required) # create your threshold type model threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) # TODO: create your own additional inputs (change if required). # create your own additional inputs additional_input = None # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, # TODO: Ensure the correct class is used below max_atoms_per_core=MyModelCurrExp._model_based_max_atoms_per_core, # These are the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # TODO: Give the model a name (shown in reports) model_name="MyModelCurrExp", # TODO: Set this to the matching binary name binary="my_model_curr_exp.aplx")
def __init__( self, n_neurons, machine_time_step, timescale_factor, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None, label=None, # neuron model parameters primary=default_parameters['primary'], # threshold types parameters v_thresh=default_parameters['v_thresh'], # initial values for the state values v_init=None, receive_port=None, receive_tag=None, board_address=None): # create your neuron model class neuron_model = SpindleModel(n_neurons, machine_time_step, primary) # create your synapse type model synapse_type = FusimotorActivation( n_neurons, machine_time_step, MuscleSpindle.default_parameters['a_syn_D'], MuscleSpindle.default_parameters['tau_syn_D'], MuscleSpindle.default_parameters['a_syn_S'], MuscleSpindle.default_parameters['tau_syn_S']) # create your input type model input_type = InputTypeCurrent() # create your threshold type model threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) # create your own additional inputs additional_input = None # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, machine_time_step=machine_time_step, timescale_factor=timescale_factor, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, # max units per core max_atoms_per_core=MuscleSpindle._model_based_max_atoms_per_core, # These are the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # model name (shown in reports) model_name="MuscleSpindle", # matching binary name binary="muscle_spindle.aplx") ReceiveBuffersToHostBasicImpl.__init__(self) self.add_constraint( TagAllocatorRequireReverseIptagConstraint( receive_port, constants.SDP_PORTS.INPUT_BUFFERING_SDP_PORT.value, board_address, receive_tag))
def __init__( self, n_neurons, spikes_per_second=None, ring_buffer_sigma=None, incoming_spike_buffer_size=None, constraints=None, label=None, # TODO: neuron model parameters (add / remove as required) # neuron model parameters my_parameter=default_parameters['my_parameter'], i_offset=default_parameters['i_offset'], # TODO: threshold types parameters (add / remove as required) # threshold types parameters v_thresh=default_parameters['v_thresh'], # TODO: synapse type parameters (add /remove as required) # synapse type parameters tau_syn_E=default_parameters['tau_syn_E'], tau_syn_I=default_parameters['tau_syn_I'], # TODO: Optionally, you can add initial values for the state # variables; this is not technically done in PyNN v_init=None): # TODO: create your neuron model class (change if required) # create your neuron model class neuron_model = MyNeuronModel( n_neurons, i_offset, my_parameter) # TODO: create your synapse type model class (change if required) # create your synapse type model synapse_type = SynapseTypeExponential( n_neurons, tau_syn_E, tau_syn_I) # TODO: create your input type model class (change if required) # create your input type model input_type = InputTypeCurrent() # TODO: create your threshold type model class (change if required) # create your threshold type model threshold_type = ThresholdTypeStatic(n_neurons, v_thresh) # TODO: create your own additional inputs (change if required). # create your own additional inputs additional_input = None # instantiate the sPyNNaker system by initialising # the AbstractPopulationVertex AbstractPopulationVertex.__init__( # standard inputs, do not need to change. self, n_neurons=n_neurons, label=label, spikes_per_second=spikes_per_second, ring_buffer_sigma=ring_buffer_sigma, incoming_spike_buffer_size=incoming_spike_buffer_size, # TODO: Ensure the correct class is used below max_atoms_per_core=MyModelCurrExp._model_based_max_atoms_per_core, # These are the various model types neuron_model=neuron_model, input_type=input_type, synapse_type=synapse_type, threshold_type=threshold_type, additional_input=additional_input, # TODO: Give the model a name (shown in reports) model_name="MyModelCurrExp", # TODO: Set this to the matching binary name binary="my_model_curr_exp.aplx")
def set_no_machine_time_steps(self, new_no_machine_time_steps): AbstractPopulationVertex.set_no_machine_time_steps( self, new_no_machine_time_steps * 2)