spynnaker8 package¶
Subpackages¶
Submodules¶
spynnaker8.spynnaker8_simulator_interface module¶
-
class
spynnaker8.spynnaker8_simulator_interface.
Spynnaker8SimulatorInterface
[source]¶ Bases:
spynnaker.pyNN.spynnaker_simulator_interface.SpynnakerSimulatorInterface
The API exposed by the simulator itself.
-
dt
¶ The timestep, in milliseconds.
-
mpi_rank
¶ The MPI rank of the controller node.
-
name
¶ The name of the simulator. Used to ensure PyNN recording neo blocks are correctly labelled.
-
num_processes
¶ The number of MPI worker processes.
-
recorders
¶ The recorders, used by the PyNN state object.
-
segment_counter
¶ The number of the current recording segment being generated.
-
t
¶ The current simulation time, in milliseconds.
-
spynnaker8.spynnaker_plotting module¶
Plotting tools to be used together with https://github.com/NeuralEnsemble/PyNN/blob/master/pyNN/utility/plotting.py
-
class
spynnaker8.spynnaker_plotting.
SpynnakerPanel
(*data, **options)[source]¶ Bases:
object
Represents a single panel in a multi-panel figure.
Compatible with
pyNN.utility.plotting.Frame
and can be mixed withpyNN.utility.plotting.Panel
Unlike
pyNN.utility.plotting.Panel
, Spikes are plotted faster, other data is plotted as a heatmapA panel is a Matplotlib Axes or Subplot instance. A data item may be an
AnalogSignal
, or a list ofSpikeTrain
s. The Panel will automatically choose an appropriate representation. Multiple data items may be plotted in the same panel.Valid options are any valid Matplotlib formatting options that should be applied to the Axes/Subplot, plus in addition:
- data_labels:
- a list of strings of the same length as the number of data items.
- line_properties:
- a list of dicts containing Matplotlib formatting options, of the same length as the number of data items.
Whole Neo Objects can be passed in as long as they contain a single Segment/run and only contain one type of data Whole Segments can be passed in only if they only contain one type of data
Parameters: - data (list(SpikeTrain) or AnalogSignal or ndarray or Block or Segment) – One or more data series to be plotted.
- options – Any additional information.
-
spynnaker8.spynnaker_plotting.
heat_plot_neo
(ax, signal_array, label='', **options)[source]¶ Plots neurons, times and values into a heatmap
Parameters: - ax (Axes) – An Axes in a matplotlib figure
- signal_array (AnalogSignal) – Neo Signal array Object
- label (str) – Label for the graph
- options – plotting options
-
spynnaker8.spynnaker_plotting.
heat_plot_numpy
(ax, data, label='', **options)[source]¶ Plots neurons, times and values into a heatmap
Parameters:
-
spynnaker8.spynnaker_plotting.
plot_segment
(axes, segment, label='', **options)[source]¶ Plots a segment into a plot of spikes or a heatmap
If there is more than ode type of Data in the segment options must include the name of the data to plotNote
method signature defined by pynn plotting. This allows mixing of this plotting tool and pynn’s
Parameters:
-
spynnaker8.spynnaker_plotting.
plot_spikes_numpy
(ax, spikes, label='', **options)[source]¶ Plot all spikes
Parameters:
-
spynnaker8.spynnaker_plotting.
plot_spiketrains
(ax, spiketrains, label='', **options)[source]¶ Plot all spike trains in a Segment in a raster plot.
Parameters: - ax (Axes) – An Axes in a matplotlib figure
- spiketrains (list(SpikeTrain)) – List of spiketimes
- label (str) – Label for the graph
- options – plotting options
Module contents¶
-
class
spynnaker8.
Cuboid
(width, height, depth)[source]¶ Bases:
pyNN.space.Shape
Represents a cuboidal volume within which neurons may be distributed.
- Arguments:
- height:
- extent in y direction
- width:
- extent in x direction
- depth:
- extent in z direction
-
spynnaker8.
distance
(src, tgt, mask=None, scale_factor=1.0, offset=0.0, periodic_boundaries=None)[source]¶ Return the Euclidian distance between two cells.
Parameters: - src –
- tgt –
- mask (ndarray) – allows only certain dimensions to be considered, e.g.:
* to ignore the z-dimension, use
mask=array([0,1])
* to ignore y,mask=array([0,2])
* to just consider z-distance,mask=array([2])
- scale_factor (float) – allows for different units in the pre- and post-position (the post-synaptic position is multiplied by this quantity).
- offset (float) –
- periodic_boundaries –
-
class
spynnaker8.
Grid2D
(aspect_ratio=1.0, dx=1.0, dy=1.0, x0=0.0, y0=0.0, z=0, fill_order='sequential', rng=None)[source]¶ Bases:
pyNN.space.BaseStructure
Represents a structure with neurons distributed on a 2D grid.
- Arguments:
- dx, dy:
- distances between points in the x, y directions.
- x0, y0:
- coordinates of the starting corner of the grid.
- z:
- the z-coordinate of all points in the grid.
- aspect_ratio:
- ratio of the number of grid points per side (not the ratio of the
side lengths, unless
dx == dy
) - fill_order:
- may be ‘sequential’ or ‘random’
-
generate_positions
(n)[source]¶ Calculate and return the positions of n neurons positioned according to this structure.
-
parameter_names
= ('aspect_ratio', 'dx', 'dy', 'x0', 'y0', 'z', 'fill_order')¶
-
class
spynnaker8.
Grid3D
(aspect_ratioXY=1.0, aspect_ratioXZ=1.0, dx=1.0, dy=1.0, dz=1.0, x0=0.0, y0=0.0, z0=0, fill_order='sequential', rng=None)[source]¶ Bases:
pyNN.space.BaseStructure
Represents a structure with neurons distributed on a 3D grid.
- Arguments:
- dx, dy, dz:
- distances between points in the x, y, z directions.
- x0, y0. z0:
- coordinates of the starting corner of the grid.
- aspect_ratioXY, aspect_ratioXZ:
- ratios of the number of grid points per side (not the ratio of the
side lengths, unless
dx == dy == dz
) - fill_order:
- may be ‘sequential’ or ‘random’.
If fill_order is ‘sequential’, the z-index will be filled first, then y then x, i.e. the first cell will be at (0,0,0) (given default values for the other arguments), the second at (0,0,1), etc.
-
generate_positions
(n)[source]¶ Calculate and return the positions of n neurons positioned according to this structure.
-
parameter_names
= ('aspect_ratios', 'dx', 'dy', 'dz', 'x0', 'y0', 'z0', 'fill_order')¶
-
class
spynnaker8.
Line
(dx=1.0, x0=0.0, y=0.0, z=0.0)[source]¶ Bases:
pyNN.space.BaseStructure
Represents a structure with neurons distributed evenly on a straight line.
- Arguments:
- dx:
- distance between points in the line.
- y, z,:
- y- and z-coordinates of all points in the line.
- x0:
- x-coordinate of the first point in the line.
-
generate_positions
(n)[source]¶ Calculate and return the positions of n neurons positioned according to this structure.
-
parameter_names
= ('dx', 'x0', 'y', 'z')¶
-
class
spynnaker8.
NumpyRNG
(seed=None, parallel_safe=True)[source]¶ Bases:
pyNN.random.WrappedRNG
Wrapper for the
numpy.random.RandomState
class (Mersenne Twister PRNG).-
translations
= {'binomial': ('binomial', {'n': 'n', 'p': 'p'}), 'exponential': ('exponential', {'beta': 'scale'}), 'gamma': ('gamma', {'k': 'shape', 'theta': 'scale'}), 'lognormal': ('lognormal', {'mu': 'mean', 'sigma': 'sigma'}), 'normal': ('normal', {'mu': 'loc', 'sigma': 'scale'}), 'normal_clipped': ('normal_clipped', {'mu': 'mu', 'sigma': 'sigma', 'low': 'low', 'high': 'high'}), 'normal_clipped_to_boundary': ('normal_clipped_to_boundary', {'mu': 'mu', 'sigma': 'sigma', 'low': 'low', 'high': 'high'}), 'poisson': ('poisson', {'lambda_': 'lam'}), 'uniform': ('uniform', {'low': 'low', 'high': 'high'}), 'uniform_int': ('randint', {'low': 'low', 'high': 'high'}), 'vonmises': ('vonmises', {'mu': 'mu', 'kappa': 'kappa'})}¶
-
-
class
spynnaker8.
RandomDistribution
(distribution, parameters_pos=None, rng=None, **parameters_named)[source]¶ Bases:
pyNN.random.RandomDistribution
Class which defines a next(n) method which returns an array of
n
random numbers from a given distribution.Parameters: - distribution (str) – the name of a random number distribution.
- parameters_pos (tuple or None) – parameters of the distribution, provided as a tuple. For the correct ordering, see random.available_distributions.
- rng (NumpyRNG or GSLRNG or NativeRNG or None) – the random number generator to use, if a specific one is desired (e.g., to provide a seed).
- parameters_named – parameters of the distribution, provided as keyword arguments.
Parameters may be provided either through
parameters_pos
or throughparameters_named
, but not both. All parameters must be provided, there are no default values. Parameter names are, in general, as used in Wikipedia.Examples:
>>> rd = RandomDistribution('uniform', (-70, -50)) >>> rd = RandomDistribution('normal', mu=0.5, sigma=0.1) >>> rng = NumpyRNG(seed=8658764) >>> rd = RandomDistribution('gamma', k=2.0, theta=5.0, rng=rng)
¶ Name Parameters Comments binomial
n
,p
gamma
k
,theta
exponential
beta
lognormal
mu
,sigma
normal
mu
,sigma
normal_clipped
mu
,sigma
,low
,high
Values outside ( low
,high
) are redrawnnormal_clipped_to_boundary
mu
,sigma
,low
,high
Values below/above low
/high
are set tolow
/high
poisson
lambda_
Trailing underscore since lambda
is a Python keyworduniform
low
,high
uniform_int
low
,high
Only generates integer values vonmises
mu
,kappa
Create a new RandomDistribution.
-
class
spynnaker8.
RandomStructure
(boundary, origin=(0.0, 0.0, 0.0), rng=None)[source]¶ Bases:
pyNN.space.BaseStructure
Represents a structure with neurons distributed randomly within a given volume.
- Arguments:
- boundary - a subclass of
Shape
. origin - the coordinates (x,y,z) of the centre of the volume.
-
generate_positions
(n)[source]¶ Calculate and return the positions of n neurons positioned according to this structure.
-
parameter_names
= ('boundary', 'origin', 'rng')¶
-
class
spynnaker8.
Space
(axes=None, scale_factor=1.0, offset=0.0, periodic_boundaries=None)[source]¶ Bases:
object
Class representing a space within distances can be calculated. The space is Cartesian, may be 1-, 2- or 3-dimensional, and may have periodic boundaries in any of the dimensions.
- Arguments:
- axes:
- if not supplied, then the 3D distance is calculated. If supplied, axes should be a string containing the axes to be used, e.g. ‘x’, or ‘yz’. axes=’xyz’ is the same as axes=None.
- scale_factor:
- it may be that the pre and post populations use different units for position, e.g. degrees and µm. In this case, scale_factor can be specified, which is applied to the positions in the post-synaptic population.
- offset:
- if the origins of the coordinate systems of the pre- and post- synaptic populations are different, offset can be used to adjust for this difference. The offset is applied before any scaling.
- periodic_boundaries:
- either None, or a tuple giving the boundaries for each dimension, e.g. ((x_min, x_max), None, (z_min, z_max)).
-
AXES
= {'x': [0], 'y': [1], 'z': [2], 'xy': [0, 1], 'yz': [1, 2], 'xz': [0, 2], 'xyz': range(0, 3), None: range(0, 3)}¶
-
distances
(A, B, expand=False)[source]¶ Calculate the distance matrix between two sets of coordinates, given the topology of the current space. From http://projects.scipy.org/pipermail/numpy-discussion/2007-April/027203.html
-
class
spynnaker8.
Sphere
(radius)[source]¶ Bases:
pyNN.space.Shape
Represents a spherical volume within which neurons may be distributed.
-
class
spynnaker8.
AllToAllConnector
(allow_self_connections=True, safe=True, verbose=None, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.all_to_all_connector.AllToAllConnector
,pyNN.connectors.AllToAllConnector
Connects all cells in the presynaptic population to all cells in the postsynaptic population
Parameters: - allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- safe (bool) – if True, check that weights and delays have valid values. If False, this check is skipped.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
-
class
spynnaker8.
ArrayConnector
(array, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.array_connector.ArrayConnector
Make connections using an array of integers based on the IDs of the neurons in the pre- and post-populations.
Parameters: - array (ndarray(2, uint8)) – an array of integers
- safe (bool) – Whether to check that weights and delays have valid values. If False, this check is skipped.
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
-
class
spynnaker8.
CSAConnector
(cset, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.csa_connector.CSAConnector
A CSA (Connection Set Algebra, Djurfeldt 2012) connector.
Parameters: - cset (csa.connset.CSet) – a connection set description
- safe (bool) – if True, check that weights and delays have valid values. If False, this check is skipped.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
-
class
spynnaker8.
DistanceDependentProbabilityConnector
(d_expression, allow_self_connections=True, safe=True, verbose=False, n_connections=None, rng=None, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.distance_dependent_probability_connector.DistanceDependentProbabilityConnector
,pyNN.connectors.DistanceDependentProbabilityConnector
Make connections using a distribution which varies with distance.
Parameters: - d_expression (str) – the right-hand side of a valid python expression for
probability, involving d, e.g.
"exp(-abs(d))"
, or"d<3"
, that can be parsed byeval()
, that computes the distance dependent distribution - allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- safe (bool) – if True, check that weights and delays have valid values. If False, this check is skipped.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- n_connections (int) – The number of efferent synaptic connections per neuron.
- rng (NumpyRNG) – random number generator
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
- d_expression (str) – the right-hand side of a valid python expression for
probability, involving d, e.g.
-
class
spynnaker8.
FixedNumberPostConnector
(n, allow_self_connections=True, safe=True, verbose=False, with_replacement=False, rng=None, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.fixed_number_post_connector.FixedNumberPostConnector
,pyNN.connectors.FixedNumberPostConnector
PyNN connector that puts a fixed number of connections on each of the post neurons.
Parameters: - n (int) – number of random post-synaptic neurons connected to pre-neurons
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- safe (bool) – Whether to check that weights and delays have valid values; if False, this check is skipped.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- with_replacement (bool) – if False, once a connection is made, it can’t be made again; if True, multiple connections between the same pair of neurons are allowed
- rng (NumpyRNG or None) – random number generator
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
-
class
spynnaker8.
FixedNumberPreConnector
(n, allow_self_connections=True, safe=True, verbose=False, with_replacement=False, rng=None, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.fixed_number_pre_connector.FixedNumberPreConnector
,pyNN.connectors.FixedNumberPreConnector
Connects a fixed number of pre-synaptic neurons selected at random, to all post-synaptic neurons.
Parameters: - n (int) – number of random pre-synaptic neurons connected to post-neurons
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- safe (bool) – Whether to check that weights and delays have valid values. If False, this check is skipped.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- with_replacement (bool) – if False, once a connection is made, it can’t be made again; if True, multiple connections between the same pair of neurons are allowed
- rng (NumpyRNG or None) – random number generator
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
-
class
spynnaker8.
FixedProbabilityConnector
(p_connect, allow_self_connections=True, safe=True, verbose=False, rng=None, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.fixed_probability_connector.FixedProbabilityConnector
,pyNN.connectors.FixedProbabilityConnector
For each pair of pre-post cells, the connection probability is constant.
Parameters: - p_connect (float) – a number between zero and one. Each potential connection is created with this probability.
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- safe (bool) – if True, check that weights and delays have valid values. If False, this check is skipped.
- space (Space) – a Space object, needed if you wish to specify distance-dependent weights or delays - not implemented
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- rng (NumpyRNG or None) – random number generator
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
-
p_connect
¶
-
class
spynnaker8.
FromFileConnector
(file, distributed=False, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker8.models.connectors.from_list_connector.FromListConnector
,pyNN.connectors.FromFileConnector
Make connections according to a list read from a file.
Parameters: - file (str or FileIO) –
Either an open file object or the filename of a file containing a list of connections, in the format required by
FromListConnector
. Column headers, if included in the file, must be specified using a list or tuple, e.g.:# columns = ["i", "j", "weight", "delay", "U", "tau_rec"]
Note that the header requires # at the beginning of the line.
- distributed (bool) –
Basic pyNN says:
if this is True, then each node will read connections from a file called filename.x, where x is the MPI rank. This speeds up loading connections for distributed simulations.Note
Always leave this as False with sPyNNaker, which is not MPI-based.
- safe (bool) – Whether to check that weights and delays have valid values. If False, this check is skipped.
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- file (str or FileIO) –
-
class
spynnaker8.
FromListConnector
(conn_list, safe=True, verbose=False, column_names=None, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.from_list_connector.FromListConnector
Make connections according to a list.
Parameters: - conn_list (list(tuple(int,int,..)) or ndarray) – a list of tuples, one tuple for each connection. Each tuple should contain: (pre_idx, post_idx, p1, p2, …, pn) where pre_idx is the index (i.e. order in the Population, not the ID) of the presynaptic neuron, post_idx is the index of the postsynaptic neuron, and p1, p2, etc. are the synaptic parameters (e.g., weight, delay, plasticity parameters).
- safe (bool) – if True, check that weights and delays have valid values. If False, this check is skipped.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- column_names (tuple(str) or list(str) or None) – the names of the parameters p1, p2, etc. If not provided, it is assumed the parameters are weight, delay (for backwards compatibility).
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
-
class
spynnaker8.
IndexBasedProbabilityConnector
(index_expression, allow_self_connections=True, rng=None, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.index_based_probability_connector.IndexBasedProbabilityConnector
Create an index-based probability connector. The index_expression must depend on the indices i, j of the populations.
Parameters: - index_expression (str) – A function of the indices of the populations, written as a Python expression; the indices will be given as variables i and j when the expression is evaluated.
- allow_self_connections (bool) – allow a neuron to connect to itself
- rng (NumpyRNG or None) – random number generator
- safe (bool) – Whether to check that weights and delays have valid values. If False, this check is skipped.
- callback (callable) –
if given, a callable that display a progress bar on the terminal.
Note
Not supported by sPyNNaker.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
-
spynnaker8.
FixedTotalNumberConnector
¶ alias of
spynnaker8.models.connectors.multapse_connector.MultapseConnector
-
class
spynnaker8.
OneToOneConnector
(safe=True, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.one_to_one_connector.OneToOneConnector
,pyNN.connectors.OneToOneConnector
Where the pre- and postsynaptic populations have the same size, connect cell i in the presynaptic population to cell i in the postsynaptic population for all i.
Parameters: - safe (bool) – if True, check that weights and delays have valid values. If False, this check is skipped.
- callback (callable) –
a function that will be called with the fractional progress of the connection routine. An example would be progress_bar.set_level.
Note
Not supported by sPyNNaker.
-
class
spynnaker8.
SmallWorldConnector
(degree, rewiring, allow_self_connections=True, n_connections=None, rng=None, safe=True, callback=None, verbose=False)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.small_world_connector.SmallWorldConnector
Create a connector that uses connection statistics based on the Small World network connectivity model. Note that this is typically used from a population to itself.
Parameters: - degree (float) – the region length where nodes will be connected locally
- rewiring (float) – the probability of rewiring each edge
- allow_self_connections (bool) – if the connector is used to connect a Population to itself, this flag determines whether a neuron is allowed to connect to itself, or only to other neurons in the Population.
- n_connections (int or None) – if specified, the number of efferent synaptic connections per neuron
- rng (NumpyRNG or None) – random number generator
- safe (bool) – Whether to check that weights and delays have valid values. If False, this check is skipped.
- callback (callable) – For PyNN compatibility only.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
-
class
spynnaker8.
KernelConnector
(shape_pre, shape_post, shape_kernel, weight_kernel=None, delay_kernel=None, shape_common=None, pre_sample_steps_in_post=None, pre_start_coords_in_post=None, post_sample_steps_in_pre=None, post_start_coords_in_pre=None, safe=True, space=None, verbose=False, callback=None)[source]¶ Bases:
spynnaker.pyNN.models.neural_projections.connectors.kernel_connector.KernelConnector
Where the pre- and post-synaptic populations are considered as a 2D array. Connect every post(row, col) neuron to many pre(row, col, kernel) through a (kernel) set of weights and/or delays.
TODO
Should these include allow_self_connections and with_replacement?
Parameters: - shape_pre (tuple(int,int)) – 2D shape of the pre population (rows/height, cols/width, usually the input image shape)
- shape_post (tuple(int,int)) – 2D shape of the post population (rows/height, cols/width)
- shape_kernel (tuple(int,int)) – 2D shape of the kernel (rows/height, cols/width)
- weight_kernel (ndarray or NumpyRNG or int or float or list(int) or list(float) or None) – (optional) 2D matrix of size shape_kernel describing the weights
- delay_kernel (ndarray or NumpyRNG or int or float or list(int) or list(float) or None) – (optional) 2D matrix of size shape_kernel describing the delays
- shape_common (tuple(int,int)) – (optional) 2D shape of common coordinate system (for both pre and post, usually the input image sizes)
- pre_sample_steps_in_post (tuple(int,int)) – (optional) Sampling steps/jumps for pre pop \(\Leftrightarrow\) \((\mathsf{step}_x, \mathsf{step}_y)\)
- pre_start_coords_in_post (tuple(int,int)) – (optional) Starting row/col for pre sampling \(\Leftrightarrow\) \((\mathsf{offset}_x, \mathsf{offset}_y)\)
- post_sample_steps_in_pre (tuple(int,int)) – (optional) Sampling steps/jumps for post pop \(\Leftrightarrow\) \((\mathsf{step}_x, \mathsf{step}_y)\)
- post_start_coords_in_pre (tuple(int,int)) – (optional) Starting row/col for post sampling \(\Leftrightarrow\) \((\mathsf{offset}_x, \mathsf{offset}_y)\)
- safe (bool) – Whether to check that weights and delays have valid values. If False, this check is skipped.
- space (Space) – Currently ignored; for future compatibility.
- verbose (bool) – Whether to output extra information about the connectivity to a CSV file
- callback (callable) – (ignored)
-
spynnaker8.
StaticSynapse
¶ alias of
spynnaker8.models.synapse_dynamics.synapse_dynamics_static.SynapseDynamicsStatic
-
spynnaker8.
STDPMechanism
¶ alias of
spynnaker8.models.synapse_dynamics.synapse_dynamics_stdp.SynapseDynamicsSTDP
-
spynnaker8.
AdditiveWeightDependence
¶ alias of
spynnaker8.models.synapse_dynamics.weight_dependence.weight_dependence_additive.WeightDependenceAdditive
-
spynnaker8.
MultiplicativeWeightDependence
¶ alias of
spynnaker8.models.synapse_dynamics.weight_dependence.weight_dependence_multiplicative.WeightDependenceMultiplicative
-
spynnaker8.
SpikePairRule
¶ alias of
spynnaker8.models.synapse_dynamics.timing_dependence.timing_dependence_spike_pair.TimingDependenceSpikePair
-
spynnaker8.
StructuralMechanismStatic
¶ alias of
spynnaker8.models.synapse_dynamics.synapse_dynamics_structural_static.SynapseDynamicsStructuralStatic
-
spynnaker8.
StructuralMechanismSTDP
¶ alias of
spynnaker8.models.synapse_dynamics.synapse_dynamics_structural_stdp.SynapseDynamicsStructuralSTDP
-
class
spynnaker8.
LastNeuronSelection
(spike_buffer_size=64)[source]¶ Bases:
spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.partner_selection.abstract_partner_selection.AbstractPartnerSelection
Partner selection that picks a random source neuron from the neurons that spiked in the last timestep
Parameters: spike_buffer_size – The size of the buffer for holding spikes -
get_parameter_names
()[source]¶ Return the names of the parameters supported by this rule
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: str
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec)[source]¶ Write the parameters of the rule to the spec
Parameters: spec (DataSpecificationGenerator) –
-
-
class
spynnaker8.
RandomSelection
[source]¶ Bases:
spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.partner_selection.abstract_partner_selection.AbstractPartnerSelection
Partner selection that picks a random source neuron from all sources
-
get_parameter_names
()[source]¶ Return the names of the parameters supported by this rule
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: str
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec)[source]¶ Write the parameters of the rule to the spec
Parameters: spec (DataSpecificationGenerator) –
-
-
class
spynnaker8.
DistanceDependentFormation
(grid=(16, 16), p_form_forward=0.16, sigma_form_forward=2.5, p_form_lateral=1.0, sigma_form_lateral=1.0)[source]¶ Bases:
spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.formation.abstract_formation.AbstractFormation
Formation rule that depends on the physical distance between neurons
Parameters: - grid (tuple(int,int) or list(int) or ndarray(int)) – (x, y) dimensions of the grid of distance
- p_form_forward (float) – The peak probability of formation on feed-forward connections
- sigma_form_forward (float) – The spread of probability with distance of formation on feed-forward connections
- p_form_lateral (float) – The peak probability of formation on lateral connections
- sigma_form_lateral (float) – The spread of probability with distance of formation on lateral connections
-
distance
(x0, x1, metric)[source]¶ Compute the distance between points x0 and x1 place on the grid using periodic boundary conditions.
Parameters: Returns: the distance
Return type:
-
generate_distance_probability_array
(probability, sigma)[source]¶ Generate the exponentially decaying probability LUTs.
Parameters: Returns: distance-dependent probabilities
Return type:
-
get_parameter_names
()[source]¶ Return the names of the parameters supported by this rule
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
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec)[source]¶ Write the parameters of the rule to the spec
Parameters: spec (DataSpecificationGenerator) –
-
class
spynnaker8.
RandomByWeightElimination
(threshold, prob_elim_depressed=0.0245, prob_elim_potentiated=0.00013600000000000003)[source]¶ Bases:
spynnaker.pyNN.models.neuron.structural_plasticity.synaptogenesis.elimination.abstract_elimination.AbstractElimination
Elimination Rule that depends on the weight of a synapse
Parameters: - threshold (float) – Below this weight is considered depression, above or equal to this weight is considered potentiation (or the static weight of the connection on static weight connections)
- prob_elim_depressed (float) – The probability of elimination if the weight has been depressed (ignored on static weight connections)
- prob_elim_potentiated (float) – The probability of elimination of the weight has been potentiated or has not changed (and also used on static weight connections)
-
get_parameter_names
()[source]¶ Return the names of the parameters supported by this rule
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
-
vertex_executable_suffix
¶ The suffix to be appended to the vertex executable for this rule
Return type: str
-
write_parameters
(spec, weight_scale)[source]¶ Write the parameters of the rule to the spec
Parameters: - spec (DataSpecificationGenerator) –
- weight_scale (float) –
-
spynnaker8.
IF_cond_exp
¶ alias of
spynnaker.pyNN.models.neuron.builds.if_cond_exp_base.IFCondExpBase
-
spynnaker8.
IF_curr_exp
¶ alias of
spynnaker.pyNN.models.neuron.builds.if_curr_exp_base.IFCurrExpBase
-
spynnaker8.
IF_curr_alpha
¶ alias of
spynnaker.pyNN.models.neuron.builds.if_curr_alpha.IFCurrAlpha
-
spynnaker8.
IF_curr_delta
¶ alias of
spynnaker.pyNN.models.neuron.builds.if_curr_delta.IFCurrDelta
-
spynnaker8.
Izhikevich
¶ alias of
spynnaker.pyNN.models.neuron.builds.izk_curr_exp_base.IzkCurrExpBase
-
class
spynnaker8.
SpikeSourceArray
(spike_times=None)[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel
-
create_vertex
(n_neurons, label, constraints)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list(AbstractConstraint) or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters
= {}¶
-
-
class
spynnaker8.
SpikeSourcePoisson
(rate=1.0, start=0, duration=None)[source]¶ Bases:
spynnaker.pyNN.models.abstract_pynn_model.AbstractPyNNModel
-
create_vertex
(n_neurons, label, constraints, seed, max_rate)[source]¶ Create a vertex for a population of the model
Parameters: - n_neurons (int) – The number of neurons in the population
- label (str) – The label to give to the vertex
- constraints (list(AbstractConstraint) or None) – A list of constraints to give to the vertex, or None
Returns: An application vertex for the population
Return type:
-
default_population_parameters
= {'max_rate': None, 'seed': None}¶
-
-
class
spynnaker8.
Assembly
(*populations, **kwargs)[source]¶ Bases:
pyNN.common.populations.Assembly
A group of neurons, may be heterogeneous, in contrast to a Population where all the neurons are of the same type.
Parameters: - populations (Population or PopulationView) – the populations or views to form the assembly out of
- kwargs – may contain label (a string describing the assembly)
Create an Assembly of Populations and/or PopulationViews.
-
class
spynnaker8.
Population
(size, cellclass, cellparams=None, structure=None, initial_values=None, label=None, constraints=None, additional_parameters=None)[source]¶ Bases:
spynnaker.pyNN.models.pynn_population_common.PyNNPopulationCommon
,spynnaker8.models.recorder.Recorder
,spynnaker8.models.populations.population_base.PopulationBase
PyNN 0.8/0.9 population object.
Parameters: - size (int) – The number of neurons in the population
- cellclass (type or AbstractPyNNModel) – The implementation of the individual neurons.
- cellparams (dict) – Parameters to pass to
cellclass
if it is a class to instantiate. - structure (BaseStructure) –
- initial_values (dict(str,float)) – Initial values of state variables
- label (str) – A label for the population
- constraints (list(AbstractConstraint)) – Any constraints on how the population is deployed to SpiNNaker.
- additional_parameters (dict(str, ..)) – Additional parameters to pass to the vertex creation function.
-
can_record
(variable)[source]¶ Determine whether variable can be recorded from this population.
Parameters: variable (str) – The variable to answer the question about Return type: bool
-
celltype
¶ Implements the PyNN expected celltype property
Returns: The celltype this property has been set to Return type: AbstractPyNNModel
-
static
create
(cellclass, cellparams=None, n=1)[source]¶ Pass through method to the constructor defined by PyNN. Create
n
cells all of the same type. Returns a Population object.Parameters: Returns: A New Population
Return type:
-
describe
(template='population_default.txt', engine='default')[source]¶ Returns a human-readable description of the population.
The output may be customized by specifying a different template together with an associated template engine (see
pyNN.descriptions
).If template is None, then a dictionary containing the template context will be returned.
Parameters: Return type:
-
find_units
(variable)[source]¶ Get the units of a variable
Parameters: variable (str) – The name of the variable Returns: The units of the variable Return type: str
-
get_data
(variables='all', gather=True, clear=False, annotations=None)[source]¶ Return a Neo Block containing the data (spikes, state variables) recorded from the Assembly.
Parameters: - variables (str or list(str)) – either a single variable name or a list of variable names. Variables must have been previously recorded, otherwise an Exception will be raised.
- gather (bool) –
Whether to collect data from all MPI nodes or just the current node.
Note
This is irrelevant on sPyNNaker, which always behaves as if this parameter is True.
- clear (bool) – Whether recorded data will be deleted from the Assembly.
- annotations (dict(str, ..)) – annotations to put on the neo block
Return type:
-
get_data_by_indexes
(variables, indexes, clear=False, annotations=None)[source]¶ Return a Neo Block containing the data (spikes, state variables) recorded from the Assembly.
Parameters: - variables (str or list(str)) – either a single variable name or a list of variable names. Variables must have been previously recorded, otherwise an Exception will be raised.
- indexes (list(int)) – List of neuron indexes to include in the
data. Clearly only neurons recording will actually have any data.
If None will be taken as all recording as in
get_data()
- clear (bool) – Whether recorded data will be deleted.
- annotations (dict(str, ..)) – annotations to put on the neo block
Return type:
-
get_initial_value
(variable, selector=None)[source]¶ See
AbstractPopulationInitializable.get_initial_value()
-
get_spike_counts
(gather=True)[source]¶ Return the number of spikes for each neuron.
Return type: ndarray
-
initialize
(**kwargs)[source]¶ Set initial values of state variables, e.g. the membrane potential. Values passed to
initialize()
may be:- single numeric values (all neurons set to the same value), or
RandomDistribution
objects, or- lists / arrays of numbers of the same size as the population mapping functions, where a mapping function accepts a single argument (the cell index) and returns a single number.
Values should be expressed in the standard PyNN units (i.e. millivolts, nanoamps, milliseconds, microsiemens, nanofarads, event per second).
Examples:
p.initialize(v=-70.0) p.initialize(v=rand_distr, gsyn_exc=0.0) p.initialize(v=lambda i: -65 + i / 10.0)
-
positions
¶ Return the position array for structured populations.
Returns: a 2D array, one row per cell. Each row is three long, for X,Y,Z Return type: ndarray
-
record
(variables, to_file=None, sampling_interval=None, indexes=None)[source]¶ Record the specified variable or variables for all cells in the Population or view.
Parameters: - variables (str or list(str)) – either a single variable name or a list of variable names. For a given celltype class, celltype.recordable contains a list of variables that can be recorded for that celltype.
- to_file (io or rawio or str) – a file to automatically record to (optional).
write_data()
will be automatically called when sim.end() is called. - sampling_interval (int) – a value in milliseconds, and an integer multiple of the simulation timestep.
- indexes (None or list(int)) – The indexes of neurons to record from. This is non-standard PyNN and equivalent to creating a view with these indexes and asking the View to record.
-
sample
(n, rng=None)[source]¶ Randomly sample n cells from the Population, and return a PopulationView object.
Parameters: Return type:
-
set
(**parameters)[source]¶ Set parameters of this population.
Parameters: parameters – The parameters to set.
-
set_initial_value
(variable, value, selector=None)[source]¶ See
AbstractPopulationInitializable.set_initial_value()
-
spinnaker_get_data
(variable)[source]¶ Public accessor for getting data as a numpy array, instead of the neo based object
Parameters: variable (str or list(str)) – either a single variable name or a list of variable names. Variables must have been previously recorded, otherwise an Exception will be raised. Returns: array of the data Return type: ndarray
-
write_data
(io, variables='all', gather=True, clear=False, annotations=None)[source]¶ Write recorded data to file, using one of the file formats supported by Neo.
Parameters: - io (io or rawio or str) – a Neo IO instance, or a string for where to put a neo instance
- variables (str or list(str)) – either a single variable name or a list of variable names. Variables must have been previously recorded, otherwise an Exception will be raised.
- gather (bool) –
Whether to bring all relevant data together.
Note
SpiNNaker always gathers.
- clear (bool) – clears the storage data if set to true after reading it back
- annotations (dict(str, ..)) – annotations to put on the neo block
-
class
spynnaker8.
PopulationView
(parent, selector, label=None)[source]¶ Bases:
spynnaker8.models.populations.population_base.PopulationBase
A view of a subset of neurons within a
Population
.In most ways, Populations and PopulationViews have the same behaviour, i.e., they can be recorded, connected with Projections, etc. It should be noted that any changes to neurons in a PopulationView will be reflected in the parent Population and vice versa.
It is possible to have views of views.
Note
Selector to Id is actually handled by
AbstractSized
.Parameters: - parent (Population or PopulationView) – the population or view to make the view from
- selector (None or slice or int or list(bool) or list(int) or ndarray(bool) or ndarray(int)) –
a slice or numpy mask array. The mask array should either be a boolean array (ideally) of the same size as the parent, or an integer array containing cell indices, i.e. if p.size == 5 then:
PopulationView(p, array([False, False, True, False, True])) PopulationView(p, array([2, 4])) PopulationView(p, slice(2, 5, 2))
will all create the same view.
- label (str) – A label for the view
-
all_cells
¶ An array containing the cell IDs of all neurons in the Population (all MPI nodes).
Return type: list(IDMixin)
-
can_record
(variable)[source]¶ Determine whether variable can be recorded from this population.
Return type: bool
-
celltype
¶ The type of neurons making up the underlying Population.
Return type: AbstractPyNNModel
-
conductance_based
¶ Indicates whether the post-synaptic response is modelled as a change in conductance or a change in current.
Return type: bool
-
describe
(template='populationview_default.txt', engine='default')[source]¶ Returns a human-readable description of the population view.
The output may be customized by specifying a different template together with an associated template engine (see pyNN.descriptions).
If template is None, then a dictionary containing the template context will be returned.
Parameters: Return type:
-
find_units
(variable)[source]¶ Get the units of a variable
Warning
NO PyNN description of this method.
Parameters: variable (str) – The name of the variable Returns: The units of the variable Return type: str
-
get
(parameter_names, gather=False, simplify=True)[source]¶ Get the values of the given parameters for every local cell in the population, or, if gather=True, for all cells in the population.
Values will be expressed in the standard PyNN units (i.e. millivolts, nanoamps, milliseconds, microsiemens, nanofarads, event per second).
-
get_data
(variables='all', gather=True, clear=False, annotations=None)[source]¶ Return a Neo Block containing the data(spikes, state variables) recorded from the Population.
Parameters: - variables (str or list(str)) – Either a single variable name or a list of variable names. Variables must have been previously recorded, otherwise an Exception will be raised.
- gather (bool) –
For parallel simulators, if gather is True, all data will be gathered to all nodes and the Neo Block will contain data from all nodes. Otherwise, the Neo Block will contain only data from the cells simulated on the local node.
Note
SpiNNaker always gathers.
- clear (bool) – If True, recorded data will be deleted from the Population.
- annotations (dict(str, ..)) – annotations to put on the neo block
Return type:
-
get_spike_counts
(gather=True)[source]¶ Returns a dict containing the number of spikes for each neuron.
The dict keys are neuron IDs, not indices.
Note
Implementation of this method is different to Population as the Populations uses PyNN 7 version of the get_spikes method which does not support indexes.
Note
SpiNNaker always gathers.
Return type: dict(int,int)
-
grandparent
¶ Returns the parent Population at the root of the tree (since the immediate parent may itself be a PopulationView).
The name “grandparent” is of course a little misleading, as it could be just the parent, or the great, great, great, …, grandparent.
Return type: Population
-
id_to_index
(id)[source]¶ Given the ID(s) of cell(s) in the PopulationView, return its / their index / indices(order in the PopulationView).
assert pv.id_to_index(pv[3]) == 3
-
index_in_grandparent
(indices)[source]¶ Given an array of indices, return the indices in the parent population at the root of the tree.
-
initial_values
¶ A dict containing the initial values of the state variables.
Return type: dict(str, ..)
-
initialize
(**initial_values)[source]¶ Set initial values of state variables, e.g. the membrane potential. Values passed to
initialize()
may be:- single numeric values (all neurons set to the same value), or
RandomDistribution
objects, or- lists / arrays of numbers of the same size as the population mapping functions, where a mapping function accepts a single argument (the cell index) and returns a single number.
Values should be expressed in the standard PyNN units( i.e. millivolts, nanoamps, milliseconds, microsiemens, nanofarads, event per second).
Examples:
p.initialize(v=-70.0) p.initialize(v=rand_distr, gsyn_exc=0.0) p.initialize(v=lambda i: -65 + i / 10.0)
-
mask
¶ The selector mask that was used to create this view.
Return type: None or slice or int or list(bool) or list(int) or ndarray(bool) or ndarray(int)
-
parent
¶ A reference to the parent Population (that this is a view of).
Return type: Population
-
record
(variables, to_file=None, sampling_interval=None)[source]¶ Record the specified variable or variables for all cells in the Population or view.
Parameters: - variables (str or list(str)) – either a single variable name, or a list of variable
names, or
all
to record everything. For a given celltype class, celltype.recordable contains a list of variables that can be recorded for that celltype. - to_file (io or rawio or str) – If specified, should be a Neo IO instance and
write_data()
will be automatically called when sim.end() is called. - sampling_interval (int) – should be a value in milliseconds, and an integer multiple of the simulation timestep.
- variables (str or list(str)) – either a single variable name, or a list of variable
names, or
-
sample
(n, rng=None)[source]¶ Randomly sample n cells from the Population view, and return a new PopulationView object.
Parameters: Return type:
-
set
(**parameters)[source]¶ Set one or more parameters for every cell in the population. Values passed to set() may be:
- single values,
RandomDistribution
objects, or- lists / arrays of values of the same size as the population mapping functions, where a mapping function accepts a single argument (the cell index) and returns a single value.
Here, a “single value” may be either a single number or a list / array of numbers (e.g. for spike times).
Values should be expressed in the standard PyNN units (i.e. millivolts, nanoamps, milliseconds, microsiemens, nanofarads, event per second).
Examples:
p.set(tau_m=20.0, v_rest=-65). p.set(spike_times=[0.3, 0.7, 0.9, 1.4]) p.set(cm=rand_distr, tau_m=lambda i: 10 + i / 10.0)
-
write_data
(io, variables='all', gather=True, clear=False, annotations=None)[source]¶ Write recorded data to file, using one of the file formats supported by Neo.
Parameters: - io (io or rawio or str) – a Neo IO instance
- variables (str or list(str)) – either a single variable name or a list of variable names. These must have been previously recorded, otherwise an Exception will be raised.
- gather (bool) –
For parallel simulators, if this is True, all data will be gathered to the master node and a single output file created there. Otherwise, a file will be written on each node, containing only data from the cells simulated on that node.
Note
SpiNNaker always gathers.
- clear (bool) – If this is True, recorded data will be deleted from the Population.
- annotations (dict(str, ..)) – should be a dict containing simple data types such as numbers and strings. The contents will be written into the output data file as metadata.
-
spynnaker8.
SpiNNakerProjection
¶ alias of
spynnaker8.models.projection.Projection
-
spynnaker8.
end
(_=True)[source]¶ Cleans up the SpiNNaker machine and software
Parameters: _ – was named compatible_output, which we don’t care about, so is a non-existent parameter Return type: None
-
spynnaker8.
setup
(timestep=0.1, min_delay='auto', max_delay='auto', graph_label=None, database_socket_addresses=None, extra_algorithm_xml_paths=None, extra_mapping_inputs=None, extra_mapping_algorithms=None, extra_pre_run_algorithms=None, extra_post_run_algorithms=None, extra_load_algorithms=None, time_scale_factor=None, n_chips_required=None, n_boards_required=None, **extra_params)[source]¶ The main method needed to be called to make the PyNN 0.8 setup. Needs to be called before any other function
Parameters: - timestep (float) – the time step of the simulations
- min_delay (float or str) – the min delay of the simulation
- max_delay (float or str) – the max delay of the simulation
- graph_label (str or None) – the label for the graph
- database_socket_addresses (iterable(SocketAddress)) – the sockets used by external devices for the database notification protocol
- extra_algorithm_xml_paths (list(str) or None) – list of paths to where other XML are located
- extra_mapping_inputs (dict(str, Any) or None) – other inputs used by the mapping process
- extra_mapping_algorithms (list(str) or None) – other algorithms to be used by the mapping process
- extra_pre_run_algorithms (list(str) or None) – extra algorithms to use before a run
- extra_post_run_algorithms (list(str) or None) – extra algorithms to use after a run
- extra_load_algorithms (list(str) or None) – extra algorithms to use within the loading phase
- time_scale_factor (int or None) – multiplicative factor to the machine time step (does not affect the neuron models accuracy)
- n_chips_required (int or None) – Deprecated! Use n_boards_required instead. Must be None if n_boards_required specified.
- n_boards_required (int or None) – if you need to be allocated a machine (for spalloc) before building your graph, then fill this in with a general idea of the number of boards you need so that the spalloc system can allocate you a machine big enough for your needs.
- extra_params – other keyword argumets used to configure PyNN
Returns: MPI rank (always 0 on SpiNNaker)
Return type: Raises: ConfigurationException – if both
n_chips_required
andn_boards_required
are used.
-
spynnaker8.
run
(simtime, callbacks=None)[source]¶ The run() function advances the simulation for a given number of milliseconds, e.g.:
Parameters: - simtime (float) – time to run for (in milliseconds)
- callbacks – callbacks to run
Returns: the actual simulation time that the simulation stopped at
Return type:
-
spynnaker8.
run_until
(tstop)[source]¶ Run until a (simulation) time period has completed.
Parameters: tstop (float) – the time to stop at (in milliseconds) Returns: the actual simulation time that the simulation stopped at Return type: float
-
spynnaker8.
run_for
(simtime, callbacks=None)¶ The run() function advances the simulation for a given number of milliseconds, e.g.:
Parameters: - simtime (float) – time to run for (in milliseconds)
- callbacks – callbacks to run
Returns: the actual simulation time that the simulation stopped at
Return type:
-
spynnaker8.
num_processes
()[source]¶ The number of MPI processes.
Note
Always 1 on SpiNNaker, which doesn’t use MPI.
Returns: the number of MPI processes
-
spynnaker8.
rank
()[source]¶ The MPI rank of the current node.
Note
Always 0 on SpiNNaker, which doesn’t use MPI.
Returns: MPI rank
-
spynnaker8.
reset
(annotations=None)[source]¶ Resets the simulation to t = 0
Parameters: annotations (dict(str, ..)) – the annotations to the data objects Return type: None
-
spynnaker8.
set_number_of_neurons_per_core
(neuron_type, max_permitted)[source]¶ Sets a ceiling on the number of neurons of a given type that can be placed on a single core.
Parameters: Return type:
-
spynnaker8.
get_projections_data
(projection_data)[source]¶ Parameters: projection_data (dict(PyNNProjectionCommon, list(int) or tuple(int) or None)) – the projection to attributes mapping Returns: a extracted data object with get method for getting the data Return type: ExtractedData
-
spynnaker8.
Projection
(presynaptic_population, postsynaptic_population, connector, synapse_type=None, source=None, receptor_type='excitatory', space=None, label=None)[source]¶ Used to support PEP 8 spelling correctly
Parameters: - presynaptic_population (Population) – the source pop
- postsynaptic_population (Population) – the dest pop
- connector (AbstractConnector) – the connector type
- synapse_type (AbstractStaticSynapseDynamics) – the synapse type
- source (None) – Unsupported; must be None
- receptor_type (str) – the receptor type
- space (Space or None) – the space object
- label (str or None) – the label
Returns: a projection object for SpiNNaker
Return type: Projection
-
spynnaker8.
get_current_time
()[source]¶ Gets the time within the simulation
Returns: returns the current time
-
spynnaker8.
create
(cellclass, cellparams=None, n=1)[source]¶ Builds a population with certain params
Parameters: Return type:
-
spynnaker8.
connect
(pre, post, weight=0.0, delay=None, receptor_type=None, p=1, rng=None)[source]¶ Builds a projection
Parameters: - pre (Population) – source pop
- post (Population) – destination pop
- weight (float) – weight of the connections
- delay (float) – the delay of the connections
- receptor_type (str) – excitatory / inhibitory
- p (float) – probability
- rng (NumpyRNG) – random number generator
Return type:
-
spynnaker8.
get_time_step
()[source]¶ The integration time step
Returns: get the time step of the simulation (in ms)
-
spynnaker8.
get_min_delay
()[source]¶ The minimum allowed synaptic delay; delays will be clamped to be at least this.
Returns: returns the min delay of the simulation
-
spynnaker8.
get_max_delay
()[source]¶ The maximum allowed synaptic delay; delays will be clamped to be at most this.
Returns: returns the max delay of the simulation
-
spynnaker8.
initialize
(cells, **initial_values)[source]¶ Sets cells to be initialised to the given values
Parameters: - cells (Population or PopulationView or Assembly) – the cells to change params on
- initial_values – the params and their values to change
Return type:
-
spynnaker8.
list_standard_models
()[source]¶ Return a list of all the StandardCellType classes available for this simulator.
Return type: list(str)
-
spynnaker8.
num_processes
()[source] The number of MPI processes.
Note
Always 1 on SpiNNaker, which doesn’t use MPI.
Returns: the number of MPI processes
-
spynnaker8.
record
(variables, source, filename, sampling_interval=None, annotations=None)[source]¶ Sets variables to be recorded.
Parameters: - variables (str or list(str)) – may be either a single variable name or a list of variable names. For a given celltype class, celltype.recordable contains a list of variables that can be recorded for that celltype.
- source (Population or PopulationView) – where to record from
- filename (str) – file name to write data to
- sampling_interval – how often to sample the recording, not ignored so far
- annotations (dict(str, ..)) – the annotations to data writers
Returns: neo object
Return type:
-
spynnaker8.
record_v
(source, filename)[source]¶ Deprecated method for getting voltage. This is not documented in the public facing API.
Parameters: - source (Population or PopulationView or Assembly) – the population / view / assembly to record
- filename (str) – the neo file to write to
Return type:
-
spynnaker8.
record_gsyn
(source, filename)[source]¶ Deprecated method for getting both types of gsyn. This is not documented in the public facing API
Parameters: - source (Population or PopulationView or Assembly) – the population / view / assembly to record
- filename (str) – the neo file to write to
Return type: