Source code for pyNN.connectors

"""
Defines a common implementation of the built-in PyNN Connector classes.

Simulator modules may use these directly, or may implement their own versions
for improved performance.

:copyright: Copyright 2006-2024 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
"""

from .random import RandomDistribution, AbstractRNG, NumpyRNG
from .core import IndexBasedExpression
from . import errors, descriptions
from .recording import files
from .parameters import LazyArray
from .standardmodels import StandardSynapseType
import numpy as np
from itertools import repeat
import logging
from copy import copy, deepcopy

# the following imports are for use within eval()
from lazyarray import arccos, arcsin, arctan, arctan2, ceil, cos, cosh, exp, \
    fabs, floor, fmod, hypot, ldexp, log, log10, modf, power, \
    sin, sinh, sqrt, tan, tanh, maximum, minimum  # noqa: F401
from numpy import e, pi  # noqa: F401

try:
    import csa
    haveCSA = True
except ImportError:
    haveCSA = False

logger = logging.getLogger("PyNN")


def _get_rng(rng):
    if isinstance(rng, AbstractRNG):
        return rng
    elif rng is None:
        return NumpyRNG(seed=151985012)
    else:
        raise Exception("rng must be either None, or a subclass of pyNN.random.AbstractRNG")


[docs] class Connector(object): """ Base class for connectors. All connector sub-classes have the following optional keyword arguments: `location_selector`: TO DO `safe`: if True, check that weights and delays have valid values. If False, this check is skipped. `callback`: a function that will be called with the fractional progress of the connection routine. An example would be `progress_bar.set_level`. """ def __init__(self, location_selector=None, safe=True, callback=None): """ docstring needed """ self.safe = safe self.callback = callback self.location_selector = location_selector if callback is not None: assert callable(callback)
[docs] def connect(self, projection): raise NotImplementedError()
[docs] def get_parameters(self): P = {} for name in self.parameter_names: P[name] = getattr(self, name) return P
def _generate_distance_map(self, projection): position_generators = (projection.pre.position_generator, projection.post.position_generator) return LazyArray(projection.space.distance_generator(*position_generators), shape=projection.shape) def _parameters_from_synapse_type(self, projection, distance_map=None): """ Obtain the parameters to be used for the connections from the projection's `synapse_type` attribute. Each parameter value is a `LazyArray`. """ if distance_map is None: distance_map = self._generate_distance_map(projection) parameter_space = projection.synapse_type.native_parameters # TODO: in the documentation, we claim that a parameter value can be # a list or 1D array of the same length as the number of connections. # We do not currently handle this scenario, although it is only # really useful for fixed-number connectors anyway. # Probably the best solution is to remove the parameter at this stage, # then set it after the connections have already been created. parameter_space.shape = (projection.pre.size, projection.post.size) for name, map in parameter_space.items(): if callable(map.base_value): if isinstance(map.base_value, IndexBasedExpression): # Assumes map is a function of index and hence requires the projection to # determine its value. It and its index function are copied so as to be able # to set the projection without altering the connector, which would perhaps # not be expected from the 'connect' call. new_map = copy(map) new_map.base_value = copy(map.base_value) new_map.base_value.projection = projection parameter_space[name] = new_map else: # Assumes map is a function of distance parameter_space[name] = map(distance_map) return parameter_space
[docs] def describe(self, template='connector_default.txt', engine='default'): """ Returns a human-readable description of the connection method. The output may be customized by specifying a different template togther with an associated template engine (see ``pyNN.descriptions``). If template is None, then a dictionary containing the template context will be returned. """ context = {'name': self.__class__.__name__, 'parameters': self.get_parameters()} return descriptions.render(engine, template, context)
class MapConnector(Connector): """ Abstract base class for Connectors based on connection maps, where a map is a 2D lazy array containing either the (boolean) connectivity matrix (aka adjacency matrix, connection set mask, etc.) or the values of a synaptic connection parameter. """ def _standard_connect(self, projection, connection_map_generator, distance_map=None): """ `connection_map_generator` should be a function or other callable, with one optional argument `mask`, which returns an iterable. The iterable should produce one element per post-synaptic neuron. Each element should be either: (i) a boolean array, indicating which of the pre-synaptic neurons should be connected to, (ii) an integer array indicating the same thing using indices, (iii) or a single boolean, meaning connect to all/none. The `mask` argument, a boolean array, can be used to limit processing to just neurons which exist on the local MPI node. todo: explain the argument `distance_map`. """ column_indices = np.arange(projection.post.size) postsynaptic_indices = projection.post.id_to_index(projection.post.all_cells) if (projection.synapse_type.native_parameters.parallel_safe or hasattr(self, "rng") and self.rng.parallel_safe): # If any of the synapse parameters are based on parallel-safe random number generators, # we need to iterate over all post-synaptic cells, so we can generate then # throw away the random numbers for the non-local nodes. logger.debug("Parallel-safe iteration.") components = ( column_indices, postsynaptic_indices, projection.post._mask_local, connection_map_generator()) else: # Otherwise, we only need to iterate over local post-synaptic cells. mask = projection.post._mask_local components = ( column_indices[mask], postsynaptic_indices[mask], repeat(True), connection_map_generator(mask)) parameter_space = self._parameters_from_synapse_type(projection, distance_map) # Loop over columns of the connection_map array # (equivalent to looping over post-synaptic neurons) for count, (col, postsynaptic_index, local, source_mask) in enumerate(zip(*components)): # `col`: column index # `postsynaptic_index`: index of the post-synaptic neuron # `local`: boolean - does the post-synaptic neuron exist on this MPI node # `source_mask`: boolean numpy array, indicating which of the pre-synaptic neurons # should be connected to, or a single boolean, meaning connect to # all/none of the pre-synaptic neurons. # It can also be an array of addresses. _proceed = False if source_mask is True or source_mask.any(): _proceed = True elif type(source_mask) == np.ndarray: if source_mask.dtype == bool: if source_mask.any(): _proceed = True elif len(source_mask) > 0: _proceed = True if _proceed: # Convert from boolean to integer mask, if necessary if source_mask is True: source_mask = np.arange(projection.pre.size, dtype=int) elif source_mask.dtype == bool: source_mask = source_mask.nonzero()[0] # Evaluate the lazy arrays containing the synaptic parameters connection_parameters = {} for name, map in parameter_space.items(): if map.is_homogeneous: connection_parameters[name] = map.evaluate(simplify=True) else: connection_parameters[name] = map[source_mask, col] # Check that parameter values are valid if self.safe: # it might be cheaper to do the weight and delay check before evaluating the # larray, however this is challenging to do if the base value is a function or # if there are a lot of operations, so for simplicity we do the check after # evaluation syn = projection.synapse_type if hasattr(syn, "parameter_checks"): for parameter_name, check in syn.parameter_checks.items(): native_parameter_name = syn.translations[parameter_name]["translated_name"] # noqa:E501 # note that for delays we should also apply units scaling to the check # values, since this currently only affects Brian we can probably # handle that separately (for weights, checks are all based on zero) if native_parameter_name in connection_parameters: check(connection_parameters[native_parameter_name], projection) if local: # Connect the neurons projection._convergent_connect( source_mask, postsynaptic_index, location_selector=self.location_selector, **connection_parameters) if self.callback: self.callback(count / projection.post.local_size) def _connect_with_map(self, projection, connection_map, distance_map=None): """ Create connections according to a connection map. Arguments: `projection`: the `Projection` that is being created. `connection_map`: a boolean `LazyArray` of the same shape as `projection`, representing the connectivity matrix. `distance_map`: TODO """ logger.debug("Connecting %s using a connection map" % projection.label) self._standard_connect(projection, connection_map.by_column, distance_map) def _get_connection_map_no_self_connections(self, projection): from pyNN.common import Population if (isinstance(projection.pre, Population) and isinstance(projection.post, Population) and projection.pre == projection.post): # special case, expected to be faster than the default, below connection_map = LazyArray(lambda i, j: i != j, shape=projection.shape) else: # this could be optimized by checking parent or component populations # but should handle both views and assemblies a = np.broadcast_to(projection.pre.all_cells, (projection.post.size, projection.pre.size)).T b = projection.post.all_cells connection_map = LazyArray(a != b, shape=projection.shape) return connection_map def _get_connection_map_no_mutual_connections(self, projection): from pyNN.common import Population if (isinstance(projection.pre, Population) and isinstance(projection.post, Population) and projection.pre == projection.post): connection_map = LazyArray(lambda i, j: i > j, shape=projection.shape) else: raise NotImplementedError("todo") return connection_map
[docs] class AllToAllConnector(MapConnector): """ Connects all cells in the presynaptic population to all cells in the postsynaptic population. Takes any of the standard :class:`Connector` optional arguments and, in addition: `allow_self_connections`: 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. """ parameter_names = ('allow_self_connections',) def __init__(self, allow_self_connections=True, location_selector=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert isinstance(allow_self_connections, bool) self.allow_self_connections = allow_self_connections def connect(self, projection): if not self.allow_self_connections: connection_map = self._get_connection_map_no_self_connections(projection) elif self.allow_self_connections == 'NoMutual': connection_map = self._get_connection_map_no_mutual_connections(projection) else: connection_map = LazyArray(True, shape=projection.shape) self._connect_with_map(projection, connection_map)
[docs] class FixedProbabilityConnector(MapConnector): """ For each pair of pre-post cells, the connection probability is constant. Takes any of the standard :class:`Connector` optional arguments and, in addition: `p_connect`: a float between zero and one. Each potential connection is created with this probability. `allow_self_connections`: 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. `rng`: an :class:`RNG` instance used to evaluate whether connections exist """ parameter_names = ('allow_self_connections', 'p_connect') def __init__(self, p_connect, allow_self_connections=True, location_selector=None, rng=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert isinstance(allow_self_connections, bool) or allow_self_connections == 'NoMutual' self.allow_self_connections = allow_self_connections self.p_connect = float(p_connect) assert 0 <= self.p_connect self.rng = _get_rng(rng) def connect(self, projection): random_map = LazyArray(RandomDistribution('uniform', (0, 1), rng=self.rng), projection.shape) connection_map = random_map < self.p_connect if not self.allow_self_connections: mask = self._get_connection_map_no_self_connections(projection) connection_map *= mask elif self.allow_self_connections == 'NoMutual': mask = self._get_connection_map_no_mutual_connections(projection) connection_map *= mask self._connect_with_map(projection, connection_map)
[docs] class DistanceDependentProbabilityConnector(MapConnector): """ For each pair of pre-post cells, the connection probability depends on distance. Takes any of the standard :class:`Connector` optional arguments and, in addition: `d_expression`: the right-hand side of a valid Python expression for probability, involving 'd', e.g. "exp(-abs(d))", or "d<3" `allow_self_connections`: 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. `rng`: an :class:`RNG` instance used to evaluate whether connections exist """ parameter_names = ('allow_self_connections', 'd_expression') def __init__(self, d_expression, allow_self_connections=True, location_selector=None, rng=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert isinstance(d_expression, str) or callable(d_expression) assert isinstance(allow_self_connections, bool) or allow_self_connections == 'NoMutual' try: if isinstance(d_expression, str): d = 0 # noqa: F841 (`d` is used in eval) assert 0 <= eval(d_expression), eval(d_expression) d = 1e12 # noqa: F841 assert 0 <= eval(d_expression), eval(d_expression) except ZeroDivisionError as err: raise ZeroDivisionError("Error in the distance expression %s. %s" % (d_expression, err)) self.d_expression = d_expression self.allow_self_connections = allow_self_connections self.distance_function = eval("lambda d: %s" % self.d_expression) self.rng = _get_rng(rng) def connect(self, projection): distance_map = self._generate_distance_map(projection) probability_map = self.distance_function(distance_map) random_map = LazyArray(RandomDistribution('uniform', (0, 1), rng=self.rng), projection.shape) connection_map = random_map < probability_map if not self.allow_self_connections: mask = self._get_connection_map_no_self_connections(projection) connection_map *= mask elif self.allow_self_connections == 'NoMutual': mask = self._get_connection_map_no_mutual_connections(projection) connection_map *= mask self._connect_with_map(projection, connection_map, distance_map)
[docs] class IndexBasedProbabilityConnector(MapConnector): """ For each pair of pre-post cells, the connection probability depends on an arbitrary functions that takes the indices of the pre and post populations. Takes any of the standard :class:`Connector` optional arguments and, in addition: `index_expression`: a function that takes the two cell indices as inputs and calculates the probability matrix from it. `allow_self_connections`: 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. `rng`: an :class:`RNG` instance used to evaluate whether connections exist """ parameter_names = ('allow_self_connections', 'index_expression') def __init__(self, index_expression, allow_self_connections=True, location_selector=None, rng=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert callable(index_expression) assert isinstance(index_expression, IndexBasedExpression) assert isinstance(allow_self_connections, bool) or allow_self_connections == 'NoMutual' self.index_expression = index_expression self.allow_self_connections = allow_self_connections self.rng = _get_rng(rng) def connect(self, projection): # The index function is copied so as to avoid the connector being altered by the "connect" # function, which is probably unexpected behaviour. index_expression = copy(self.index_expression) index_expression.projection = projection probability_map = LazyArray(index_expression, projection.shape) random_map = LazyArray(RandomDistribution('uniform', (0, 1), rng=self.rng), projection.shape) connection_map = random_map < probability_map if not self.allow_self_connections: mask = self._get_connection_map_no_self_connections(projection) connection_map *= mask elif self.allow_self_connections == 'NoMutual': mask = self._get_connection_map_no_mutual_connections(projection) connection_map *= mask self._connect_with_map(projection, connection_map)
[docs] class DisplacementDependentProbabilityConnector(IndexBasedProbabilityConnector): """ For each pair of pre-post cells, the connection probability depends on the displacement of the two neurons, i.e. on the triplet (dx, dy, dz) where dx is the distance between the x-coordinates of the two neurons, and so on. Takes any of the standard :class:`Connector` optional arguments and, in addition: `disp_function`: the right-hand side of a valid Python expression for probability, involving an array named 'd' whose first dimension has size 3. e.g. "(d[0] < 3) * (d[1] < 2) * exp(-abs(d[2]))" `allow_self_connections`: 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. `rng`: an :class:`RNG` instance used to evaluate whether connections exist """ class DisplacementExpression(IndexBasedExpression): """ A displacement based expression function used to determine the connection probability and the value of variable connection parameters of a projection """ def __init__(self, disp_function): """ `disp_function`: a function that takes a 3xN numpy displacement matrix and maps each row (displacement) to a probability between 0 and 1 """ self._disp_function = disp_function def __call__(self, i, j): disp = (self.projection.post.positions.T[j] - self.projection.pre.positions.T[i]).T return self._disp_function(disp) def __init__(self, disp_function, allow_self_connections=True, location_selector=None, rng=None, safe=True, callback=None): super(DisplacementDependentProbabilityConnector, self).__init__( self.DisplacementExpression(disp_function), allow_self_connections=allow_self_connections, rng=rng, callback=callback)
[docs] class FromListConnector(Connector): """ Make connections according to a list. Arguments: `conn_list`: 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). `column_names`: the names of the parameters p1, p2, etc. If not provided, it is assumed the parameters are 'weight', 'delay' (for backwards compatibility). This should be specified using a tuple. `safe`: if True, check that weights and delays have valid values. If False, this check is skipped. `callback`: if True, display a progress bar on the terminal. """ parameter_names = ('conn_list',) def __init__(self, conn_list, column_names=None, location_selector=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe=safe, callback=callback) self.conn_list = np.array(conn_list) if len(conn_list) > 0: n_columns = self.conn_list.shape[1] if column_names is None: if n_columns == 2: self.column_names = () elif n_columns == 4: self.column_names = ('weight', 'delay') else: raise TypeError("Argument 'column_names' is required.") else: self.column_names = column_names if n_columns != len(self.column_names) + 2: raise ValueError(f"connection list has {n_columns - 2} parameter columns, " f"but {len(self.column_names)} column names provided.") else: self.column_names = () def connect(self, projection): """Connect-up a Projection.""" logger.debug("conn_list (original) = \n%s", self.conn_list) synapse_parameter_names = projection.synapse_type.get_parameter_names() for name in self.column_names: if name not in synapse_parameter_names: raise ValueError("%s is not a valid parameter for %s" % ( name, projection.synapse_type.__class__.__name__)) if self.conn_list.size == 0: return if np.any(self.conn_list[:, 0] >= projection.pre.size): raise errors.ConnectionError("source index out of range") # need to do some profiling, to figure out the best way to do this: # - order of sorting/filtering by local # - use np.unique, or just do in1d(self.conn_list)? idx = np.argsort(self.conn_list[:, 1]) targets = np.unique(self.conn_list[:, 1]).astype(int) local = np.in1d(targets, np.arange(projection.post.size)[projection.post._mask_local], assume_unique=True) local_targets = targets[local] self.conn_list = self.conn_list[idx] left = np.searchsorted(self.conn_list[:, 1], local_targets, 'left') right = np.searchsorted(self.conn_list[:, 1], local_targets, 'right') logger.debug("idx = %s", idx) logger.debug("targets = %s", targets) logger.debug("local_targets = %s", local_targets) logger.debug("conn_list (sorted by target) = \n%s", self.conn_list) logger.debug("left = %s", left) logger.debug("right = %s", right) for tgt, l, r in zip(local_targets, left, right): sources = self.conn_list[l:r, 0].astype(int) connection_parameters = deepcopy(projection.synapse_type.parameter_space) connection_parameters.shape = (r - l,) for col, name in enumerate(self.column_names, 2): connection_parameters.update(**{name: self.conn_list[l:r, col]}) if isinstance(projection.synapse_type, StandardSynapseType): connection_parameters = projection.synapse_type.translate( connection_parameters) connection_parameters.evaluate() projection._convergent_connect(sources, tgt, location_selector=self.location_selector, **connection_parameters)
[docs] class FromFileConnector(FromListConnector): """ Make connections according to a list read from a file. Arguments: `file`: 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`: 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. `safe`: if True, check that weights and delays have valid values. If False, this check is skipped. `callback`: if True, display a progress bar on the terminal. """ parameter_names = ('file', 'distributed') def __init__(self, file, distributed=False, location_selector=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe=safe, callback=callback) if isinstance(file, str): file = files.StandardTextFile(file, mode='r') self.file = file self.distributed = distributed def connect(self, projection): """Connect-up a Projection.""" if self.distributed: self.file.rename("%s.%d" % (self.file.name, projection._simulator.state.mpi_rank)) self.column_names = self.file.get_metadata().get('columns', ('weight', 'delay')) for ignore in "ij": if ignore in self.column_names: self.column_names.remove(ignore) self.conn_list = self.file.read() FromListConnector.connect(self, projection)
class FixedNumberConnector(MapConnector): # base class - should not be instantiated parameter_names = ('allow_self_connections', 'n') def __init__(self, n, allow_self_connections=True, with_replacement=False, location_selector=None, rng=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert isinstance(allow_self_connections, bool) or allow_self_connections == 'NoMutual' self.allow_self_connections = allow_self_connections self.with_replacement = with_replacement self.n = n if isinstance(n, int): assert n >= 0 elif isinstance(n, RandomDistribution): # weak check that the random distribution is ok err_msg = "the random distribution produces negative numbers" assert np.all(np.array(n.next(100)) >= 0), err_msg else: raise TypeError("n must be an integer or a RandomDistribution object") self.rng = _get_rng(rng) def _rng_uniform_int_exclude(self, n, size, exclude): res = self.rng.next(n, 'uniform_int', {"low": 0, "high": size}, mask=None) logger.debug("RNG0 res=%s" % res) idx = np.where(res == exclude)[0] logger.debug("RNG1 exclude=%d, res=%s idx=%s" % (exclude, res, idx)) while idx.size > 0: redrawn = self.rng.next(idx.size, 'uniform_int', {"low": 0, "high": size}, mask=None) res[idx] = redrawn idx = idx[np.where(res == exclude)[0]] logger.debug("RNG2 exclude=%d redrawn=%s res=%s idx=%s" % (exclude, redrawn, res, idx)) return res
[docs] class FixedNumberPostConnector(FixedNumberConnector): """ Each pre-synaptic neuron is connected to exactly `n` post-synaptic neurons chosen at random. The sampling behaviour is controlled by the `with_replacement` argument. "With replacement" means that each post-synaptic neuron is chosen from the entire population. There is always therefore a possibility of multiple connections between a given pair of neurons. "Without replacement" means that once a neuron has been selected, it cannot be selected again until the entire population has been selected. This means that if `n` is less than the size of the post-synaptic population, there are no multiple connections. If `n` is greater than the size of the post- synaptic population, all possible single connections are made before starting to add duplicate connections. Takes any of the standard :class:`Connector` optional arguments and, in addition: `n`: either a positive integer, or a `RandomDistribution` that produces positive integers. If `n` is a `RandomDistribution`, then the number of post-synaptic neurons is drawn from this distribution for each pre-synaptic neuron. `with_replacement`: if True, the selection of neurons to connect is made from the entire population. If False, once a neuron is selected it cannot be selected again until the entire population has been connected. `allow_self_connections`: 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. `rng`: an :class:`RNG` instance used to evaluate which potential connections are created. """ def _get_num_post(self): if isinstance(self.n, int): n_post = self.n else: n_post = self.n.next() return n_post def connect(self, projection): connections = [[] for i in range(projection.post.size)] for source_index in range(projection.pre.size): n = self._get_num_post() if self.with_replacement: if not self.allow_self_connections and projection.pre == projection.post: targets = self._rng_uniform_int_exclude(n, projection.post.size, source_index) else: targets = self.rng.next( n, 'uniform_int', {"low": 0, "high": projection.post.size}, mask=None) else: all_cells = np.arange(projection.post.size) if not self.allow_self_connections and projection.pre == projection.post: all_cells = all_cells[all_cells != source_index] full_sets = n // all_cells.size remainder = n % all_cells.size target_sets = [] if full_sets > 0: target_sets = [all_cells] * full_sets if remainder > 0: target_sets.append(self.rng.permutation(all_cells)[:remainder]) targets = np.hstack(target_sets) assert targets.size == n for target_index in targets: connections[target_index].append(source_index) def build_source_masks(mask=None): if mask is None: return [np.array(x) for x in connections] else: return [np.array(x) for x in np.array(connections)[mask]] self._standard_connect(projection, build_source_masks)
[docs] class FixedNumberPreConnector(FixedNumberConnector): """ Each post-synaptic neuron is connected to exactly `n` pre-synaptic neurons chosen at random. The sampling behaviour is controlled by the `with_replacement` argument. "With replacement" means that each pre-synaptic neuron is chosen from the entire population. There is always therefore a possibility of multiple connections between a given pair of neurons. "Without replacement" means that once a neuron has been selected, it cannot be selected again until the entire population has been selected. This means that if `n` is less than the size of the pre-synaptic population, there are no multiple connections. If `n` is greater than the size of the pre- synaptic population, all possible single connections are made before starting to add duplicate connections. Takes any of the standard :class:`Connector` optional arguments and, in addition: `n`: either a positive integer, or a `RandomDistribution` that produces positive integers. If `n` is a `RandomDistribution`, then the number of pre-synaptic neurons is drawn from this distribution for each post-synaptic neuron. `with_replacement`: if True, the selection of neurons to connect is made from the entire population. If False, once a neuron is selected it cannot be selected again until the entire population has been connected. `allow_self_connections`: 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. `rng`: an :class:`RNG` instance used to evaluate which potential connections are created. """ def _get_num_pre(self, size, mask=None): if isinstance(self.n, int): if mask is None: n_pre = repeat(self.n, size) else: n_pre = repeat(self.n, mask.sum()) else: if mask is None: n_pre = self.n.next(size) else: if self.n.rng.parallel_safe: n_pre = self.n.next(size)[mask] else: n_pre = self.n.next(mask.sum()) return n_pre def connect(self, projection): if self.with_replacement: if self.allow_self_connections or projection.pre != projection.post: def build_source_masks(mask=None): n_pre = self._get_num_pre(projection.post.size, mask) for n in n_pre: sources = self.rng.next( n, 'uniform_int', {"low": 0, "high": projection.pre.size}, mask=None) assert sources.size == n yield sources else: def build_source_masks(mask=None): n_pre = self._get_num_pre(projection.post.size, mask) if self.rng.parallel_safe or mask is None: for i, n in enumerate(n_pre): sources = self._rng_uniform_int_exclude(n, projection.pre.size, i) assert sources.size == n yield sources else: # TODO: use mask to obtain indices i raise NotImplementedError( "allow_self_connections=False currently requires a parallel safe RNG.") else: if self.allow_self_connections or projection.pre != projection.post: def build_source_masks(mask=None): # where n > projection.pre.size, first all pre-synaptic cells # are connected one or more times, then the remainder # are chosen randomly n_pre = self._get_num_pre(projection.post.size, mask) all_cells = np.arange(projection.pre.size) for n in n_pre: full_sets = n // projection.pre.size remainder = n % projection.pre.size source_sets = [] if full_sets > 0: source_sets = [all_cells] * full_sets if remainder > 0: source_sets.append(self.rng.permutation(all_cells)[:remainder]) sources = np.hstack(source_sets) assert sources.size == n yield sources else: def build_source_masks(mask=None): # where n > projection.pre.size, first all pre-synaptic cells # are connected one or more times, then the remainder # are chosen randomly n_pre = self._get_num_pre(projection.post.size, mask) all_cells = np.arange(projection.pre.size) if self.rng.parallel_safe or mask is None: for i, n in enumerate(n_pre): full_sets = n // (projection.pre.size - 1) remainder = n % (projection.pre.size - 1) allowed_cells = all_cells[all_cells != i] source_sets = [] if full_sets > 0: source_sets = [allowed_cells] * full_sets if remainder > 0: source_sets.append(self.rng.permutation(allowed_cells)[:remainder]) sources = np.hstack(source_sets) assert sources.size == n yield sources else: raise NotImplementedError( "allow_self_connections=False currently requires a parallel safe RNG.") self._standard_connect(projection, build_source_masks)
[docs] class OneToOneConnector(MapConnector): """ 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*. Takes any of the standard :class:`Connector` optional arguments. """ parameter_names = tuple() def connect(self, projection): """Connect-up a Projection.""" connection_map = LazyArray(lambda i, j: i == j, shape=projection.shape) self._connect_with_map(projection, connection_map)
[docs] class SmallWorldConnector(Connector): """ Connect cells so as to create a small-world network. Takes any of the standard :class:`Connector` optional arguments and, in addition: `degree`: the region length where nodes will be connected locally. `rewiring`: the probability of rewiring each edge. `allow_self_connections`: 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`: if specified, the number of efferent synaptic connections per neuron. `rng`: an :class:`RNG` instance used to evaluate which connections are created. """ parameter_names = ('allow_self_connections', 'degree', 'rewiring', 'n_connections') def __init__(self, degree, rewiring, allow_self_connections=True, n_connections=None, location_selector=None, rng=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert 0 <= rewiring <= 1 assert isinstance(allow_self_connections, bool) or allow_self_connections == 'NoMutual' self.rewiring = rewiring self.d_expression = "d < %g" % degree self.allow_self_connections = allow_self_connections self.n_connections = n_connections self.rng = _get_rng(rng) def connect(self, projection): """Connect-up a Projection.""" raise NotImplementedError
[docs] class CSAConnector(MapConnector): """ Use the Connection Set Algebra (Djurfeldt, 2012) to connect cells. Takes any of the standard :class:`Connector` optional arguments and, in addition: `cset`: a connection set object. """ parameter_names = ('cset',) if haveCSA: def __init__(self, cset, location_selector=None, safe=True, callback=None): """ """ Connector.__init__(self, location_selector, safe=safe, callback=callback) self.cset = cset arity = csa.arity(cset) assert arity in (0, 2), 'must specify mask or connection-set with arity 0 or 2' else: def __init__(self, cset, safe=True, callback=None): raise RuntimeError("CSAConnector not available---couldn't import csa module") def connect(self, projection): """Connect-up a Projection.""" # Cut out finite part c = csa.cross((0, projection.pre.size - 1), (0, projection.post.size - 1)) * \ self.cset # can't we cut out just the columns we want? if csa.arity(self.cset) == 2: # Connection-set with arity 2 for (i, j, weight, delay) in c: projection._convergent_connect([projection.pre[i]], projection.post[j], location_selector=self.location_selector, weight=weight, delay=delay) elif csa.arity(self.cset) == 0: # inefficient implementation as a starting point connection_map = np.zeros((projection.pre.size, projection.post.size), dtype=bool) for addr in c: connection_map[addr] = True self._connect_with_map(projection, LazyArray(connection_map)) else: raise NotImplementedError
[docs] class CloneConnector(MapConnector): """ Connects cells with the same connectivity pattern as a previous projection. """ parameter_names = ('reference_projection',) def __init__(self, reference_projection, safe=True, callback=None): """ Create a new CloneConnector. `reference_projection` -- the projection to clone the connectivity pattern from """ MapConnector.__init__(self, location_selector=None, safe=safe, callback=callback) self.reference_projection = reference_projection def connect(self, projection): if (projection.pre != self.reference_projection.pre or projection.post != self.reference_projection.post): raise errors.ConnectionError( "Pre and post populations must match between reference ({0}" " and {1}) and clone projections ({2} and {3}) for CloneConnector".format( self.reference_projection.pre, self.reference_projection.post, projection.pre, projection.post)) connection_map = LazyArray(~np.isnan(self.reference_projection.get(['weight'], 'array', gather='all')[0])) self._connect_with_map(projection, connection_map)
[docs] class ArrayConnector(MapConnector): """ Provide an explicit boolean connection matrix, with shape (m, n) where m is the size of the presynaptic population and n that of the postsynaptic population. """ parameter_names = ('array',) def __init__(self, array, location_selector=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) self.array = array def connect(self, projection): connection_map = LazyArray(self.array, projection.shape) self._connect_with_map(projection, connection_map)
[docs] class FixedTotalNumberConnector(FixedNumberConnector): parameter_names = ('allow_self_connections', 'n') def __init__(self, n, allow_self_connections=True, with_replacement=True, location_selector=None, rng=None, safe=True, callback=None): """ Create a new connector. """ Connector.__init__(self, location_selector, safe, callback) assert isinstance(allow_self_connections, bool) or allow_self_connections == 'NoMutual' self.allow_self_connections = allow_self_connections self.with_replacement = with_replacement self.n = n if isinstance(n, int): assert n >= 0 elif isinstance(n, RandomDistribution): # weak check that the random distribution is ok err_msg = "the random distribution produces negative numbers" assert np.all(np.array(n.next(100)) >= 0), err_msg else: raise TypeError("n must be an integer or a RandomDistribution object") self.rng = _get_rng(rng) def connect(self, projection): # This implementation is not "parallel safe" for random numbers. # todo: support the `parallel_safe` flag. # Determine number of processes and current rank rank = projection._simulator.state.mpi_rank num_processes = projection._simulator.state.num_processes # Assume that targets are equally distributed over processes targets_per_process = int(len(projection.post) / num_processes) # Calculate the number of synapses on each process bino = RandomDistribution('binomial', [self.n, targets_per_process / len(projection.post)], rng=self.rng) num_conns_on_vp = np.zeros(num_processes, dtype=int) sum_dist = 0 sum_partitions = 0 for k in range(num_processes): p_local = targets_per_process / (len(projection.post) - sum_dist) bino.parameters['p'] = p_local bino.parameters['n'] = self.n - sum_partitions num_conns_on_vp[k] = bino.next() sum_dist += targets_per_process sum_partitions += num_conns_on_vp[k] # Draw random sources and targets connections = [[] for i in range(projection.post.size)] possible_targets = np.arange(projection.post.size)[projection.post._mask_local] for i in range(num_conns_on_vp[rank]): source_index = self.rng.next(1, 'uniform_int', {"low": 0, "high": projection.pre.size}, mask=None)[0] target_index = self.rng.choice(possible_targets, size=1)[0] connections[target_index].append(source_index) def build_source_masks(mask=None): if mask is None: return [np.array(x) for x in connections] else: return [np.array(x) for x in np.array(connections)[mask]] self._standard_connect(projection, build_source_masks)