spynnaker.pyNN.models.neuron.builds package

Module contents

class spynnaker.pyNN.models.neuron.builds.EIFConductanceAlphaPopulation(**kwargs)

Bases: object

Exponential integrate and fire neuron with spike triggered and sub-threshold adaptation currents (isfa, ista reps.)

Warning

Not currently supported by the tool chain.

default_initial_values = {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -70.6, 'w': 0.0}
default_parameters = {'a': 4.0, 'b': 0.0805, 'cm': 0.281, 'delta_T': 2.0, 'e_rev_E': 0.0, 'e_rev_I': -80.0, 'i_offset': 0.0, 'tau_m': 9.3667, 'tau_refrac': 0.1, 'tau_syn_E': 5.0, 'tau_syn_I': 0.5, 'tau_w': 144.0, 'v_reset': -70.6, 'v_rest': -70.6, 'v_spike': -40.0, 'v_thresh': -50.4}
class spynnaker.pyNN.models.neuron.builds.HHCondExp(**kwargs)

Bases: object

Single-compartment Hodgkin-Huxley model with exponentially decaying current input.

Warning

Not currently supported by the tool chain.

default_initial_values = {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}
default_parameters = {'cm': 0.2, 'e_rev_E': 0.0, 'e_rev_I': -80, 'e_rev_K': -90.0, 'e_rev_Na': 50.0, 'e_rev_leak': -65.0, 'g_leak': 0.01, 'gbar_K': 6.0, 'gbar_Na': 20.0, 'i_offset': 0.0, 'tau_syn_E': 0.2, 'tau_syn_I': 2.0, 'v_offset': -63}
class spynnaker.pyNN.models.neuron.builds.IFCondAlpha(**kwargs)

Bases: object

Leaky integrate and fire neuron with an alpha-shaped current input.

Warning

Not currently supported by the tool chain.

default_initial_values = {'gsyn_exc': 0.0, 'gsyn_inh': 0.0, 'v': -65.0}
default_parameters = {'cm': 1.0, 'e_rev_E': 0.0, 'e_rev_I': -70.0, 'i_offset': 0, 'tau_m': 20, 'tau_refrac': 0.1, 'tau_syn_E': 0.3, 'tau_syn_I': 0.5, 'v_reset': -65.0, 'v_rest': -65.0, 'v_thresh': -50.0}
class spynnaker.pyNN.models.neuron.builds.IFCondExpBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying conductance input.

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)

  • e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCondExpStoc(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with a stochastic threshold.

Habenschuss S, Jonke Z, Maass W. Stochastic computations in cortical microcircuit models. PLoS Computational Biology. 2013;9(11):e1003311. doi:10.1371/journal.pcbi.1003311

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)

  • e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)

  • du_th (float, iterable(float), RandomDistribution or (mapping) function) – \(du_{thresh}\)

  • tau_th (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{thresh}\)

  • v (Float, float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrAlpha(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an alpha-shaped current-based input.

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • exc_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{linear}_e\)

  • exc_exp_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{exponential}_e\)

  • inh_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{linear}_i\)

  • inh_exp_response (float, iterable(float), RandomDistribution or (mapping) function) – \(response^\mathrm{exponential}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrDelta(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input.

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

Type:

isyn_inh: float, iterable(float), RandomDistribution or (mapping) function

class spynnaker.pyNN.models.neuron.builds.IFCurrDeltaCa2Adaptive(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input.

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • tau_ca2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{\mathrm{Ca}^{+2}}\)

  • i_ca2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{\mathrm{Ca}^{+2}}\)

  • i_alpha (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_\alpha\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

Type:

isyn_inh: float, iterable(float), RandomDistribution or (mapping) function

class spynnaker.pyNN.models.neuron.builds.IFCurrDualExpBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with two exponentially decaying excitatory current inputs, and one exponentially decaying inhibitory current input.

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_1}\)

  • tau_syn_E2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_2}\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_1}\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • isyn_exc2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_2}\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrExpBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input.

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrExpCa2Adaptive(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Model from Liu, Y. H., & Wang, X. J. (2001). Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron. Journal of Computational Neuroscience, 10(1), 25-45. doi:10.1023/A:1008916026143

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • tau_ca2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{\mathrm{Ca}^{+2}}\)

  • i_ca2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{\mathrm{Ca}^{+2}}\)

  • i_alpha (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_\alpha\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFCurrExpSEMDBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input, where the excitatory input depends upon the inhibitory input (see https://www.cit-ec.de/en/nbs/spiking-insect-vision)

Parameters:

  • tau_m (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_m\)

  • cm (float, iterable(float), RandomDistribution or (mapping) function) – \(C_m\)

  • v_rest (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{rest}\)

  • v_reset (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{reset}\)

  • v_thresh (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{thresh}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_1}\)

  • tau_syn_E2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_2}\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • tau_refrac (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau_{refrac}\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(V_{init}\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_1}\)

  • isyn_exc2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_2}\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • multiplicator (float, iterable(float), RandomDistribution or (mapping) function) –

  • exc2_old (float, iterable(float), RandomDistribution or (mapping) function) –

  • scaling_factor (float, iterable(float), RandomDistribution or (mapping) function) –

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IFFacetsConductancePopulation(**kwargs)

Bases: object

Leaky integrate and fire neuron with conductance-based synapses and fixed threshold as it is resembled by the FACETS Hardware Stage 1.

Warning

Not currently supported by the tool chain.

default_initial_values = {'v': -65.0}
default_parameters = {'e_rev_I': -80, 'g_leak': 40.0, 'tau_syn_E': 30.0, 'tau_syn_I': 30.0, 'v_reset': -80.0, 'v_rest': -65.0, 'v_thresh': -55.0}
class spynnaker.pyNN.models.neuron.builds.IzkCondDualExpBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Izhikevich neuron model with conductance inputs and dual synapse.

Parameters:

  • a (float, iterable(float), RandomDistribution or (mapping) function) – \(a\)

  • b (float, iterable(float), RandomDistribution or (mapping) function) – \(b\)

  • c (float, iterable(float), RandomDistribution or (mapping) function) – \(c\)

  • d (float, iterable(float), RandomDistribution or (mapping) function) – \(d\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • u (float, iterable(float), RandomDistribution or (mapping) function) – \(u_{init} = \delta V_{init}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(v_{init} = V_{init}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_E2 (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_{e_2}\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)

  • e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_exc2 (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_{e_2}\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IzkCondExpBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Izhikevich neuron model with conductance inputs.

Parameters:

  • a (float, iterable(float), RandomDistribution or (mapping) function) – \(a\)

  • b (float, iterable(float), RandomDistribution or (mapping) function) – \(b\)

  • c (float, iterable(float), RandomDistribution or (mapping) function) – \(c\)

  • d (float, iterable(float), RandomDistribution or (mapping) function) – \(d\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • u (float, iterable(float), RandomDistribution or (mapping) function) – \(u_{init} = \delta V_{init}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(v_{init} = V_{init}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • e_rev_E (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_e\)

  • e_rev_I (float, iterable(float), RandomDistribution or (mapping) function) – \(E^{rev}_i\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs

class spynnaker.pyNN.models.neuron.builds.IzkCurrExpBase(**kwargs)

Bases: AbstractPyNNNeuronModelStandard

Izhikevich neuron model with current inputs.

Parameters:

  • a (float, iterable(float), RandomDistribution or (mapping) function) – \(a\)

  • b (float, iterable(float), RandomDistribution or (mapping) function) – \(b\)

  • c (float, iterable(float), RandomDistribution or (mapping) function) – \(c\)

  • d (float, iterable(float), RandomDistribution or (mapping) function) – \(d\)

  • i_offset (float, iterable(float), RandomDistribution or (mapping) function) – \(I_{offset}\)

  • u (float, iterable(float), RandomDistribution or (mapping) function) – \(u_{init} = \delta V_{init}\)

  • v (float, iterable(float), RandomDistribution or (mapping) function) – \(v_{init} = V_{init}\)

  • tau_syn_E (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_e\)

  • tau_syn_I (float, iterable(float), RandomDistribution or (mapping) function) – \(\tau^{syn}_i\)

  • isyn_exc (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_e\)

  • isyn_inh (float, iterable(float), RandomDistribution or (mapping) function) – \(I^{syn}_i\)

  • model_name (str) – Name of the model.

  • binary (str) – Name of the implementation executable.

  • neuron_model (AbstractPyNNNeuronModel) – The model of the neuron soma

  • input_type (AbstractInputType) – The model of synaptic input types

  • synapse_type (AbstractSynapseType) – The model of the synapses’ dynamics

  • threshold_type (AbstractThresholdType) – The model of the firing threshold

  • additional_input_type (AbstractAdditionalInput or None) – The model (if any) of additional environmental inputs