spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence package¶
Module contents¶
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class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
AbstractTimingDependence
[source]¶ Bases:
object
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get_parameter_names
()[source]¶ Return the names of the parameters supported by this timing dependency model.
Return type: iterable(str)
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get_parameters_sdram_usage_in_bytes
()[source]¶ Get the amount of SDRAM used by the parameters of this rule
Return type: int
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is_same_as
(timing_dependence)[source]¶ Determine if this timing dependence is the same as another
Parameters: timing_dependence (AbstractTimingDependence) – Return type: bool
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synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
Return type: AbstractSynapseStructure
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vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
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write_parameters
(spec, global_weight_scale, synapse_weight_scales)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) – The specification to write to
- global_weight_scale (float) – The weight scale applied globally
- synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale
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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)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.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: int
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is_same_as
(timing_dependence)[source]¶ Determine if this timing dependence is the same as another
Parameters: timing_dependence (AbstractTimingDependence) – Return type: bool
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synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
Return type: AbstractSynapseStructure
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vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
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write_parameters
(spec, global_weight_scale, synapse_weight_scales)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) – The specification to write to
- global_weight_scale (float) – The weight scale applied globally
- synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale
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class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
TimingDependencePfisterSpikeTriplet
(tau_plus, tau_minus, tau_x, tau_y, A_plus, A_minus)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.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: int
-
is_same_as
(timing_dependence)[source]¶ Determine if this timing dependence is the same as another
Parameters: timing_dependence (AbstractTimingDependence) – Return type: bool
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
Return type: AbstractSynapseStructure
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec, global_weight_scale, synapse_weight_scales)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) – The specification to write to
- global_weight_scale (float) – The weight scale applied globally
- synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale
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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)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.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: int
-
is_same_as
(timing_dependence)[source]¶ Determine if this timing dependence is the same as another
Parameters: timing_dependence (AbstractTimingDependence) – Return type: bool
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
Return type: AbstractSynapseStructure
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec, global_weight_scale, synapse_weight_scales)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) – The specification to write to
- global_weight_scale (float) – The weight scale applied globally
- synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale
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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)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.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: int
-
is_same_as
(timing_dependence)[source]¶ Determine if this timing dependence is the same as another
Parameters: timing_dependence (AbstractTimingDependence) – Return type: bool
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
Return type: AbstractSynapseStructure
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec, global_weight_scale, synapse_weight_scales)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) – The specification to write to
- global_weight_scale (float) – The weight scale applied globally
- synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale
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class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
TimingDependenceVogels2011
(alpha, tau=20.0, A_plus=0.01, A_minus=0.01)[source]¶ Bases:
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.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: int
-
is_same_as
(timing_dependence)[source]¶ Determine if this timing dependence is the same as another
Parameters: timing_dependence (AbstractTimingDependence) – Return type: bool
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
Return type: AbstractSynapseStructure
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec, global_weight_scale, synapse_weight_scales)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) – The specification to write to
- global_weight_scale (float) – The weight scale applied globally
- synapse_weight_scales (list(float)) – The total weight scale applied to each synapse including the global weight scale
-