spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence package

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

Return type:int
get_provenance_data(pre_population_label, post_population_label)[source]

Get any provenance data

Parameters:
  • pre_population_label (str) – label of pre.
  • post_population_label (str) – label of post.
Return type:

iterable(ProvenanceDataItem)

is_same_as(timing_dependence)[source]

Determine if this timing dependence is the same as another

Parameters:timing_dependence (AbstractTimingDependence) –
Return type:bool
n_weight_terms

The number of weight terms expected by this timing rule

Return type:int
pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

Return type:int
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, weight_scales)[source]

Write the parameters of the rule to the spec

Parameters:
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:
  • tau_plus (float) – \(\tau_+\)
  • tau_minus (float) – \(\tau_-\)
  • A_plus (float) – \(A^+\)
  • A_minus (float) – \(A^-\)
A_minus

\(A^-\)

Return type:float
A_plus

\(A^+\)

Return type:float
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
n_weight_terms

The number of weight terms expected by this timing rule

Return type:int
pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

Return type:int
synaptic_structure

Get the synaptic structure of the plastic part of the rows

Return type:AbstractSynapseStructure
tau_minus

\(\tau_-\)

Return type:float
tau_plus

\(\tau_+\)

Return type:float
vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

Return type:str
write_parameters(spec, weight_scales)[source]

Write the parameters of the rule to the spec

Parameters:
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:
  • tau_plus (float) – \(\tau_+\)
  • tau_minus (float) – \(\tau_-\)
  • tau_x (float) – \(\tau_x\)
  • tau_y (float) – \(\tau_y\)
  • A_plus (float) – \(A^+\)
  • A_minus (float) – \(A^-\)
A_minus

\(A^-\)

Return type:float
A_plus

\(A^+\)

Return type:float
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
n_weight_terms

The number of weight terms expected by this timing rule

Return type:int
pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

Return type:int
synaptic_structure

Get the synaptic structure of the plastic part of the rows

Return type:AbstractSynapseStructure
tau_minus

\(\tau_-\)

Return type:float
tau_plus

\(\tau_+\)

Return type:float
tau_x

\(\tau_x\)

Return type:float
tau_y

\(\tau_y\)

Return type:float
vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

Return type:str
write_parameters(spec, weight_scales)[source]

Write the parameters of the rule to the spec

Parameters:
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:
  • accumulator_depression (int) –
  • accumulator_potentiation (int) –
  • mean_pre_window (float) –
  • mean_post_window (float) –
  • dual_fsm (bool) –
  • A_plus (float) – \(A^+\)
  • A_minus (float) – \(A^-\)
A_minus

\(A^-\)

Return type:float
A_plus

\(A^+\)

Return type:float
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
n_weight_terms

The number of weight terms expected by this timing rule

Return type:int
pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

Return type:int
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, weight_scales)[source]

Write the parameters of the rule to the spec

Parameters:
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:
  • tau_plus (float) – \(\tau_+\)
  • tau_minus (float) – \(\tau_-\)
  • A_plus (float) – \(A^+\)
  • A_minus (float) – \(A^-\)
A_minus

\(A^-\)

Return type:float
A_plus

\(A^+\)

Return type:float
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
n_weight_terms

The number of weight terms expected by this timing rule

Return type:int
pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

Return type:int
synaptic_structure

Get the synaptic structure of the plastic part of the rows

Return type:AbstractSynapseStructure
tau_minus

\(\tau_-\)

Return type:float
tau_plus

\(\tau_+\)

Return type:float
vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

Return type:str
write_parameters(spec, weight_scales)[source]

Write the parameters of the rule to the spec

Parameters:
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:
  • alpha (float) – \(\alpha\)
  • tau (float) – \(\tau\)
  • A_plus (float) – \(A^+\)
  • A_minus (float) – \(A^-\)
A_minus

\(A^-\)

Return type:float
A_plus

\(A^+\)

Return type:float
alpha

\(\alpha\)

Return type:float
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
n_weight_terms

The number of weight terms expected by this timing rule

Return type:int
pre_trace_n_bytes

The number of bytes used by the pre-trace of the rule per neuron

Return type:int
synaptic_structure

Get the synaptic structure of the plastic part of the rows

Return type:AbstractSynapseStructure
tau

\(\tau\)

Return type:float
vertex_executable_suffix

The suffix to be appended to the vertex executable for this rule

Return type:str
write_parameters(spec, weight_scales)[source]

Write the parameters of the rule to the spec

Parameters: