spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence package¶
Submodules¶
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence module¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.abstract_timing_dependence.
AbstractTimingDependence
[source]¶ Bases:
object
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_pfister_spike_triplet module¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_pfister_spike_triplet.
TimingDependencePfisterSpikeTriplet
(tau_plus, tau_minus, tau_x, tau_y)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus
¶
-
tau_plus
¶
-
tau_x
¶
-
tau_y
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent module¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_recurrent.
TimingDependenceRecurrent
(accumulator_depression=-6, accumulator_potentiation=6, mean_pre_window=35.0, mean_post_window=35.0, dual_fsm=True)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_nearest_pair module¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_nearest_pair.
TimingDependenceSpikeNearestPair
(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus
¶
-
tau_plus
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_pair module¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_spike_pair.
TimingDependenceSpikePair
(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus
¶
-
tau_plus
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_vogels_2011 module¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.timing_dependence_vogels_2011.
TimingDependenceVogels2011
(alpha, tau=20.0)[source]¶ -
-
alpha
¶
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
Module contents¶
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
AbstractTimingDependence
[source]¶ Bases:
object
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
TimingDependenceSpikePair
(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus
¶
-
tau_plus
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
TimingDependencePfisterSpikeTriplet
(tau_plus, tau_minus, tau_x, tau_y)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus
¶
-
tau_plus
¶
-
tau_x
¶
-
tau_y
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
-
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)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
TimingDependenceSpikeNearestPair
(tau_plus=20.0, tau_minus=20.0)[source]¶ -
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau_minus
¶
-
tau_plus
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-
-
class
spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence.
TimingDependenceVogels2011
(alpha, tau=20.0)[source]¶ -
-
alpha
¶
-
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
-
n_weight_terms
¶ The number of weight terms expected by this timing rule
-
pre_trace_n_bytes
¶ The number of bytes used by the pre-trace of the rule per neuron
-
synaptic_structure
¶ Get the synaptic structure of the plastic part of the rows
-
tau
¶
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
-