spynnaker.pyNN.models.neuron.builds package

Submodules

spynnaker.pyNN.models.neuron.builds.eif_cond_alpha_isfa_ista module

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

Bases: object

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

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}

spynnaker.pyNN.models.neuron.builds.hh_cond_exp module

class spynnaker.pyNN.models.neuron.builds.hh_cond_exp.HHCondExp(**kwargs)

Bases: object

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

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}

spynnaker.pyNN.models.neuron.builds.if_cond_alpha module

class spynnaker.pyNN.models.neuron.builds.if_cond_alpha.IFCondAlpha(**kwargs)

Bases: object

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

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}

spynnaker.pyNN.models.neuron.builds.if_cond_exp_base module

class spynnaker.pyNN.models.neuron.builds.if_cond_exp_base.IFCondExpBase(**kwargs)

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

spynnaker.pyNN.models.neuron.builds.if_cond_exp_stoc module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with a stochastic threshold.

spynnaker.pyNN.models.neuron.builds.if_curr_alpha module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

spynnaker.pyNN.models.neuron.builds.if_curr_delta module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input

spynnaker.pyNN.models.neuron.builds.if_curr_dual_exp_base module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

spynnaker.pyNN.models.neuron.builds.if_curr_exp_base module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input

spynnaker.pyNN.models.neuron.builds.if_curr_exp_ca2_adaptive module

class spynnaker.pyNN.models.neuron.builds.if_curr_exp_ca2_adaptive.IFCurrExpCa2Adaptive(**kwargs)

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.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

spynnaker.pyNN.models.neuron.builds.if_curr_exp_semd_base module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.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)

spynnaker.pyNN.models.neuron.builds.if_facets_hardware1 module

class spynnaker.pyNN.models.neuron.builds.if_facets_hardware1.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

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}

spynnaker.pyNN.models.neuron.builds.izk_cond_exp_base module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

spynnaker.pyNN.models.neuron.builds.izk_curr_exp_base module

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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.)

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.

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.

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: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an exponentially decaying current input

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

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.IzkCondExpBase(**kwargs)

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with a stochastic threshold.

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.AbstractPyNNNeuronModelStandard

Leaky integrate and fire neuron with an instantaneous current input

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.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

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

Bases: spynnaker.pyNN.models.neuron.abstract_pynn_neuron_model_standard.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)