spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence package¶
Module contents¶
- class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.AbstractTimingDependence¶
Bases:
object
- abstract get_parameter_names()[source]¶
Return the names of the parameters supported by this timing dependency model.
- Return type:
iterable(str)
- abstract get_parameters_sdram_usage_in_bytes()[source]¶
Get the amount of SDRAM used by the parameters of this rule.
- Return type:
- abstract is_same_as(timing_dependence)[source]¶
Determine if this timing dependence is the same as another.
- Parameters:
timing_dependence (AbstractTimingDependence) –
- Return type:
- abstract property n_weight_terms¶
The number of weight terms expected by this timing rule.
- Return type:
- abstract property pre_trace_n_bytes¶
The number of bytes used by the pre-trace of the rule per neuron.
- Return type:
- abstract property synaptic_structure¶
The synaptic structure of the plastic part of the rows.
- Return type:
- abstract property vertex_executable_suffix¶
The suffix to be appended to the vertex executable for this rule.
- Return type:
- class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependencePfisterSpikeTriplet(tau_plus, tau_minus, tau_x, tau_y, A_plus, A_minus)¶
Bases:
AbstractTimingDependence
A timing dependence STDP rule based on spike triplets.
Jean-Pascal Pfister, Wulfram Gerstner. Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity. Journal of Neuroscience, 20 September 2006, 26 (38) 9673-9682; DOI: 10.1523/JNEUROSCI.1425-06.2006
- Parameters:
- get_parameter_names()[source]¶
Return the names of the parameters supported by this timing dependency model.
- Return type:
iterable(str)
- get_parameters_sdram_usage_in_bytes()[source]¶
Get the amount of SDRAM used by the parameters of this rule.
- Return type:
- is_same_as(timing_dependence)[source]¶
Determine if this timing dependence is the same as another.
- Parameters:
timing_dependence (AbstractTimingDependence) –
- Return type:
- property pre_trace_n_bytes¶
The number of bytes used by the pre-trace of the rule per neuron.
- Return type:
- property synaptic_structure¶
The synaptic structure of the plastic part of the rows.
- Return type:
- property vertex_executable_suffix¶
The suffix to be appended to the vertex executable for this rule.
- Return type:
- class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceRecurrent(accumulator_depression=-6, accumulator_potentiation=6, mean_pre_window=35.0, mean_post_window=35.0, dual_fsm=True, A_plus=0.01, A_minus=0.01)¶
Bases:
AbstractTimingDependence
A timing dependence STDP rule based on recurrences.
- Parameters:
- default_parameters = {'accumulator_depression': -6, 'accumulator_potentiation': 6, 'dual_fsm': True, 'mean_post_window': 35.0, 'mean_pre_window': 35.0}¶
- get_parameter_names()[source]¶
Return the names of the parameters supported by this timing dependency model.
- Return type:
iterable(str)
- get_parameters_sdram_usage_in_bytes()[source]¶
Get the amount of SDRAM used by the parameters of this rule.
- Return type:
- is_same_as(timing_dependence)[source]¶
Determine if this timing dependence is the same as another.
- Parameters:
timing_dependence (AbstractTimingDependence) –
- Return type:
- property pre_trace_n_bytes¶
The number of bytes used by the pre-trace of the rule per neuron.
- Return type:
- property synaptic_structure¶
The synaptic structure of the plastic part of the rows.
- Return type:
- property vertex_executable_suffix¶
The suffix to be appended to the vertex executable for this rule.
- Return type:
- class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceSpikeNearestPair(tau_plus=20.0, tau_minus=20.0, A_plus=0.01, A_minus=0.01)¶
Bases:
AbstractTimingDependence
A timing dependence STDP rule based on nearest pairs.
- Parameters:
- default_parameters = {'tau_minus': 20.0, 'tau_plus': 20.0}¶
- get_parameter_names()[source]¶
Return the names of the parameters supported by this timing dependency model.
- Return type:
iterable(str)
- get_parameters_sdram_usage_in_bytes()[source]¶
Get the amount of SDRAM used by the parameters of this rule.
- Return type:
- is_same_as(timing_dependence)[source]¶
Determine if this timing dependence is the same as another.
- Parameters:
timing_dependence (AbstractTimingDependence) –
- Return type:
- property pre_trace_n_bytes¶
The number of bytes used by the pre-trace of the rule per neuron.
- Return type:
- property synaptic_structure¶
The synaptic structure of the plastic part of the rows.
- Return type:
- property vertex_executable_suffix¶
The suffix to be appended to the vertex executable for this rule.
- Return type:
- class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceSpikePair(tau_plus=20.0, tau_minus=20.0, A_plus=0.01, A_minus=0.01)¶
Bases:
AbstractTimingDependence
A basic timing dependence STDP rule.
- Parameters:
- get_parameter_names()[source]¶
Return the names of the parameters supported by this timing dependency model.
- Return type:
iterable(str)
- get_parameters_sdram_usage_in_bytes()[source]¶
Get the amount of SDRAM used by the parameters of this rule.
- Return type:
- is_same_as(timing_dependence)[source]¶
Determine if this timing dependence is the same as another.
- Parameters:
timing_dependence (AbstractTimingDependence) –
- Return type:
- property pre_trace_n_bytes¶
The number of bytes used by the pre-trace of the rule per neuron.
- Return type:
- property synaptic_structure¶
The synaptic structure of the plastic part of the rows.
- Return type:
- property vertex_executable_suffix¶
The suffix to be appended to the vertex executable for this rule.
- Return type:
- class spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.TimingDependenceVogels2011(alpha, tau=20.0, A_plus=0.01, A_minus=0.01)¶
Bases:
AbstractTimingDependence
A timing dependence STDP rule due to Vogels (2011).
- Parameters:
- default_parameters = {'tau': 20.0}¶
- get_parameter_names()[source]¶
Return the names of the parameters supported by this timing dependency model.
- Return type:
iterable(str)
- get_parameters_sdram_usage_in_bytes()[source]¶
Get the amount of SDRAM used by the parameters of this rule.
- Return type:
- is_same_as(timing_dependence)[source]¶
Determine if this timing dependence is the same as another.
- Parameters:
timing_dependence (AbstractTimingDependence) –
- Return type:
- property pre_trace_n_bytes¶
The number of bytes used by the pre-trace of the rule per neuron.
- Return type:
- property synaptic_structure¶
The synaptic structure of the plastic part of the rows.
- Return type:
- property vertex_executable_suffix¶
The suffix to be appended to the vertex executable for this rule.
- Return type: