spynnaker8.models package¶
Subpackages¶
- spynnaker8.models.connectors package
- Submodules
- spynnaker8.models.connectors.all_to_all_connector module
- spynnaker8.models.connectors.array_connector module
- spynnaker8.models.connectors.csa_connector module
- spynnaker8.models.connectors.distance_dependent_probability_connector module
- spynnaker8.models.connectors.fixed_number_post_connector module
- spynnaker8.models.connectors.fixed_number_pre_connector module
- spynnaker8.models.connectors.fixed_probability_connector module
- spynnaker8.models.connectors.from_file_connector module
- spynnaker8.models.connectors.from_list_connector module
- spynnaker8.models.connectors.index_based_probability_connector module
- spynnaker8.models.connectors.kernel_connector module
- spynnaker8.models.connectors.multapse_connector module
- spynnaker8.models.connectors.one_to_one_connector module
- spynnaker8.models.connectors.small_world_connector module
- Module contents
- spynnaker8.models.populations package
- spynnaker8.models.synapse_dynamics package
- Subpackages
- spynnaker8.models.synapse_dynamics.timing_dependence package
- Submodules
- spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_pfister_spike_triplet module
- spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_recurrent module
- spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_spike_nearest_pair module
- spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_spike_pair module
- spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_vogels_2011 module
- Module contents
- spynnaker8.models.synapse_dynamics.weight_dependence package
- Submodules
- spynnaker8.models.synapse_dynamics.weight_dependence.weight_dependence_additive module
- spynnaker8.models.synapse_dynamics.weight_dependence.weight_dependence_additive_triplet module
- spynnaker8.models.synapse_dynamics.weight_dependence.weight_dependence_multiplicative module
- Module contents
- spynnaker8.models.synapse_dynamics.timing_dependence package
- Submodules
- spynnaker8.models.synapse_dynamics.synapse_dynamics_static module
- spynnaker8.models.synapse_dynamics.synapse_dynamics_stdp module
- Module contents
- Subpackages
Submodules¶
spynnaker8.models.data_cache module¶
-
class
spynnaker8.models.data_cache.
DataCache
(label, description, segment_number, recording_start_time, t)[source]¶ Bases:
object
Storage object to hold all the data to (re)create a Neo Segment
Note
Required because deep-copy does not work on neo Objects
Stores the Data shared by all variable types at the top level and holds a cache for the variable specific data
Parameters: - label – cache label
- description – cache description
- segment_number – cache segment number
- recording_start_time – when this cache was started in recording space.
- t – time
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description
¶
-
get_data
(variable)[source]¶ Get the variable cache for the named variable
Parameters: variable – name of variable to get cache for Rtype variable: str Returns: The cache data, IDs, indexes and units Return type: VariableCache
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has_data
(variable)[source]¶ Checks if data for a variable has been cached
Parameters: variable (str) – Name of variable Returns: True if there is cached data Return type: bool
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label
¶
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rec_datetime
¶
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recording_start_time
¶
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save_data
(variable, data, indexes, n_neurons, units, sampling_interval)[source]¶ Saves the data for one variable in this segment
Parameters: - variable (str) – name of variable data applies to
- data (nparray) – raw data in sPyNNaker format
- indexes (nparray) – population indexes for which data should be returned
- n_neurons (int) – Number of neurons in the population. Regardless of if they where recording or not.
- units (str) – the units in which the data is
Return type: None
-
segment_number
¶
-
t
¶
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variables
¶ Provides a list of which variables data has been cached for
Return type: Iterator (str)
spynnaker8.models.projection module¶
-
class
spynnaker8.models.projection.
Projection
(pre_synaptic_population, post_synaptic_population, connector, synapse_type=None, source=None, receptor_type=None, space=None, label=None)[source]¶ Bases:
spynnaker.pyNN.models.pynn_projection_common.PyNNProjectionCommon
sPyNNaker 8 projection class
-
get
(attribute_names, format, gather=True, with_address=True, multiple_synapses='last')[source]¶ Get a parameter for PyNN 0.8
Parameters: - attribute_names (str or iterable(str)) – list of attributes to gather
- format – “list” or “array”
- gather – gather over all nodes (defaulted to true on SpiNNaker)
- with_address – True if the source and target are to be included
- multiple_synapses – What to do with the data if format=”array” and if the multiple source-target pairs with the same values exist. Currently only “last” is supported
Returns: values selected
-
label
¶
-
post
¶
-
pre
¶
-
printDelays
(file, format='list', gather=True)[source]¶ Print synaptic weights to file. In the array format, zeros are printed for non-existent connections.
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save
(attribute_names, file, format='list', gather=True, with_address=True)[source]¶ Print synaptic attributes (weights, delays, etc.) to file. In the array format, zeros are printed for non-existent connections. Values will be expressed in the standard PyNN units (i.e., millivolts, nanoamps, milliseconds, microsiemens, nanofarads, event per second).
-
spynnaker8.models.recorder module¶
-
class
spynnaker8.models.recorder.
Recorder
(population)[source]¶ Bases:
spynnaker.pyNN.models.recording_common.RecordingCommon
-
read_in_signal
(segment, block, signal_array, data_indexes, view_indexes, variable, recording_start_time, sampling_interval, units, label)[source]¶ Reads in a data item that’s not spikes (likely v, gsyn e, gsyn i) and saves this data to the segment.
Parameters: - segment (neo.Segment) – Segment to add data to
- block (neo.Block) – neo block
- signal_array (nparray) – the raw signal data
- data_indexes (list(int)) – The indexes for the recorded data
- view_indexes (list(int)) – The indexes for which data should be returned. If None all data (view_index = data_indexes)
- variable – the variable name
- recording_start_time – when recording started
- sampling_interval – how often a neuron is recorded
- units – the units of the recorded value
- label – human readable label
-
read_in_spikes
(segment, spikes, t, n_neurons, recording_start_time, sampling_interval, indexes, label)[source]¶ Converts the data into SpikeTrains and saves them to the segment.
Parameters: - segment (neo.Segment) – Segment to add spikes to
- spikes (nparray) – Spike data in raw sPyNNaker format
- t (int) – last simulation time
- n_neurons (int) – total number of neurons including ones not recording
- recording_start_time (int) – time recording started
- sampling_interval – how often a neuron is recorded
- label (str) – recording elements label
-
spynnaker8.models.variable_cache module¶
-
class
spynnaker8.models.variable_cache.
VariableCache
(data, indexes, n_neurons, units, sampling_interval)[source]¶ Bases:
object
Simple holder method to keep data, IDs, indexes and units together
Typically used to recreate the Neo object for one type of variable for one segment
Parameters: - data (nparray) – raw data in sPyNNaker format
- indexes (list (int)) – Population indexes for which data was collected
- n_neurons (int) – Number of neurons in the population, regardless of whether they were recording or not.
- units (str) – the units in which the data is
-
data
¶
-
indexes
¶
-
n_neurons
¶
-
sampling_interval
¶
-
units
¶
Module contents¶
-
class
spynnaker8.models.
Projection
(pre_synaptic_population, post_synaptic_population, connector, synapse_type=None, source=None, receptor_type=None, space=None, label=None)[source]¶ Bases:
spynnaker.pyNN.models.pynn_projection_common.PyNNProjectionCommon
sPyNNaker 8 projection class
-
get
(attribute_names, format, gather=True, with_address=True, multiple_synapses='last')[source]¶ Get a parameter for PyNN 0.8
Parameters: - attribute_names (str or iterable(str)) – list of attributes to gather
- format – “list” or “array”
- gather – gather over all nodes (defaulted to true on SpiNNaker)
- with_address – True if the source and target are to be included
- multiple_synapses – What to do with the data if format=”array” and if the multiple source-target pairs with the same values exist. Currently only “last” is supported
Returns: values selected
-
label
¶
-
post
¶
-
pre
¶
-
printDelays
(file, format='list', gather=True)[source]¶ Print synaptic weights to file. In the array format, zeros are printed for non-existent connections.
-
save
(attribute_names, file, format='list', gather=True, with_address=True)[source]¶ Print synaptic attributes (weights, delays, etc.) to file. In the array format, zeros are printed for non-existent connections. Values will be expressed in the standard PyNN units (i.e., millivolts, nanoamps, milliseconds, microsiemens, nanofarads, event per second).
-
-
class
spynnaker8.models.
Recorder
(population)[source]¶ Bases:
spynnaker.pyNN.models.recording_common.RecordingCommon
-
read_in_signal
(segment, block, signal_array, data_indexes, view_indexes, variable, recording_start_time, sampling_interval, units, label)[source]¶ Reads in a data item that’s not spikes (likely v, gsyn e, gsyn i) and saves this data to the segment.
Parameters: - segment (neo.Segment) – Segment to add data to
- block (neo.Block) – neo block
- signal_array (nparray) – the raw signal data
- data_indexes (list(int)) – The indexes for the recorded data
- view_indexes (list(int)) – The indexes for which data should be returned. If None all data (view_index = data_indexes)
- variable – the variable name
- recording_start_time – when recording started
- sampling_interval – how often a neuron is recorded
- units – the units of the recorded value
- label – human readable label
-
read_in_spikes
(segment, spikes, t, n_neurons, recording_start_time, sampling_interval, indexes, label)[source]¶ Converts the data into SpikeTrains and saves them to the segment.
Parameters: - segment (neo.Segment) – Segment to add spikes to
- spikes (nparray) – Spike data in raw sPyNNaker format
- t (int) – last simulation time
- n_neurons (int) – total number of neurons including ones not recording
- recording_start_time (int) – time recording started
- sampling_interval – how often a neuron is recorded
- label (str) – recording elements label
-