def _create_replacement_connection(c_in, c_out):
"""Generate a new Connection to replace two through a passthrough Node"""
assert c_in.post_obj is c_out.pre_obj
assert c_in.post_obj.output is None
# determine the filter for the new Connection
if c_in.synapse is None:
synapse = c_out.synapse
elif c_out.synapse is None:
synapse = c_in.synapse
else:
raise NotImplementedError("Cannot merge two filters")
# Note: the algorithm below is in the right ballpark,
# but isn't exactly the same as two low-pass filters
# filter = c_out.filter + c_in.filter
function = c_in.function
if c_out.function is not None:
raise Exception("Cannot remove a Node with a " "function being computed on it")
# compute the combined transform
transform = np.dot(full_transform(c_out), full_transform(c_in))
# check if the transform is 0 (this happens a lot
# with things like identity transforms)
if np.all(transform == 0):
return None
c = nengo.Connection(
c_in.pre_obj, c_out.post_obj, synapse=synapse, transform=transform, function=function, add_to_container=False
)
return c
def get_ensemble_sink(model, connection):
"""Get the sink for connections into an Ensemble."""
ens = model.object_operators[connection.post_obj]
if (isinstance(connection.pre_obj, nengo.Node)
and not callable(connection.pre_obj.output)
and not isinstance(connection.pre_obj.output, Process)
and connection.pre_obj.output is not None):
# Connections from constant valued Nodes are optimised out.
# Build the value that will be added to the direct input for the
# ensemble.
val = connection.pre_obj.output[connection.pre_slice]
if connection.function is not None:
val = connection.function(val)
transform = full_transform(connection, slice_pre=False)
ens.direct_input += np.dot(transform, val)
else:
# If this connection has a learning rule
if connection.learning_rule is not None:
# If the rule modifies encoders, sink it into learnt input port
modifies = connection.learning_rule.learning_rule_type.modifies
if modifies == "encoders":
return spec(ObjectPort(ens, EnsembleInputPort.learnt))
# Otherwise we just sink into the Ensemble
return spec(ObjectPort(ens, InputPort.standard))
def build_from_ensemble_connection(model, conn):
"""Build the parameters object for a connection from an Ensemble."""
if conn.solver.weights:
raise NotImplementedError(
"SpiNNaker does not currently support neuron to neuron connections"
)
# Create a random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get the transform
transform = full_transform(conn, slice_pre=False, allow_scalars=False)
# Solve for the decoders
eval_points, decoders, solver_info = connection_b.build_decoders(
model, conn, rng)
# Store the parameters in the model
model.params[conn] = BuiltConnection(decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info)
# Modify the transform if this is a global inhibition connection
if (isinstance(conn.post_obj, nengo.ensemble.Neurons)
and np.all(transform[0, :] == transform[1:, :])):
transform = np.array([transform[0]])
return EnsembleTransmissionParameters(decoders, transform)
def build_from_ensemble_connection(model, conn):
"""Build the parameters object for a connection from an Ensemble."""
if conn.solver.weights:
raise NotImplementedError(
"SpiNNaker does not currently support neuron to neuron connections"
)
# Create a random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get the transform
transform = full_transform(conn, slice_pre=False, allow_scalars=False)
# Solve for the decoders
eval_points, decoders, solver_info = connection_b.build_decoders(
model, conn, rng
)
# Store the parameters in the model
model.params[conn] = BuiltConnection(decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info)
# Modify the transform if this is a global inhibition connection
if (isinstance(conn.post_obj, nengo.ensemble.Neurons) and
np.all(transform[0, :] == transform[1:, :])):
transform = np.array([transform[0]])
return EnsembleTransmissionParameters(decoders, transform)
def get_ensemble_sink(model, connection):
"""Get the sink for connections into an Ensemble."""
ens = model.object_operators[connection.post_obj]
if (isinstance(connection.pre_obj, nengo.Node) and
not callable(connection.pre_obj.output) and
not isinstance(connection.pre_obj.output, Process) and
connection.pre_obj.output is not None):
# Connections from constant valued Nodes are optimised out.
# Build the value that will be added to the direct input for the
# ensemble.
val = connection.pre_obj.output[connection.pre_slice]
if connection.function is not None:
val = connection.function(val)
transform = full_transform(connection, slice_pre=False)
ens.direct_input += np.dot(transform, val)
else:
# If this connection has a learning rule
if connection.learning_rule is not None:
# If the rule modifies encoders, sink it into learnt input port
modifies = connection.learning_rule.learning_rule_type.modifies
if modifies == "encoders":
return spec(ObjectPort(ens, EnsembleInputPort.learnt))
# Otherwise we just sink into the Ensemble
return spec(ObjectPort(ens, InputPort.standard))
def build_from_ensemble_connection(model, conn):
"""Build the parameters object for a connection from an Ensemble."""
# Create a random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get the transform
transform = full_transform(conn, slice_pre=False)
# Use Nengo upstream to build parameters for the solver
eval_points, activities, targets = connection_b.build_linear_system(
model, conn, rng
)
# Use cached solver
solver = model.decoder_cache.wrap_solver(conn.solver)
if conn.solver.weights:
raise NotImplementedError(
"SpiNNaker does not currently support neuron to neuron connections"
)
else:
decoders, solver_info = solver(activities, targets, rng=rng)
# Return the parameters
return BuiltConnection(
decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info
)
def build_from_ensemble_connection(model, conn):
"""Build the parameters object for a connection from an Ensemble."""
if conn.solver.weights:
raise NotImplementedError(
"SpiNNaker does not currently support neuron to neuron connections"
)
# Create a random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get the transform
transform = full_transform(conn, slice_pre=False)
# Solve for the decoders
eval_points, decoders, solver_info = connection_b.build_decoders(
model, conn, rng
)
# Return the parameters
return BuiltConnection(
decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info
)
def mpi_build_network(model, network):
""" Build a nengo Network for nengo_mpi.
This function replaces nengo.builder.build_network.
For each connection that emenates from a Node, has a non-None
pre-slice, AND has no function attached to it, we replace it
with a Connection that is functionally equivalent, but has
the slicing moved into the transform. This is done because
in some such cases, the refimpl nengo builder will implement the
slicing using a python function, which we want to avoid in nengo_mpi.
Also records which Connections have probes attached to them.
Parameters
----------
model: MpiModel
The model to which created components will be added.
network: nengo.Network
The network to be built.
"""
remove_conns = []
for conn in network.connections:
replace_connection = (
isinstance(conn.pre_obj, Node) and conn.pre_slice != slice(None) and conn.function is None
)
if replace_connection:
transform = full_transform(conn)
with network:
Connection(
conn.pre_obj,
conn.post_obj,
synapse=conn.synapse,
transform=transform,
solver=conn.solver,
learning_rule_type=conn.learning_rule_type,
eval_points=conn.eval_points,
scale_eval_points=conn.scale_eval_points,
seed=conn.seed,
)
remove_conns.append(conn)
if remove_conns:
network.objects[Connection] = filter(lambda c: c not in remove_conns, network.connections)
network.connections = network.objects[Connection]
probed_connections = []
for probe in network.probes:
if isinstance(probe.target, Connection):
probed_connections.append(probe.target)
model.probed_connections |= set(probed_connections)
return builder.build_network(model, network)
def build_nrn_connection(model, conn):
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
# Check pre-conditions
assert isinstance(conn.pre, nengo.Ensemble)
assert not isinstance(conn.pre.neuron_type, nengo.neurons.Direct)
# FIXME assert no rate neurons are used. How to do that?
# Get input signal
# FIXME this should probably be
# model.sig[conn]['in'] = model.sig[conn.pre]["out"]
# in both cases
if isinstance(conn.pre, nengo.ensemble.Neurons):
model.sig[conn]["in"] = model.sig[conn.pre.ensemble]["out"]
else:
model.sig[conn]["in"] = model.sig[conn.pre]["out"]
# Figure out type of connection
if isinstance(conn.post, nengo.ensemble.Neurons):
raise NotImplementedError() # TODO
elif isinstance(conn.post.neuron_type, Compartmental):
pass
else:
raise AssertionError("This function should only be called if post neurons are " "compartmental.")
# Solve for weights
# FIXME just assuming solver is a weight solver, may that break?
# Default solver should probably also produce sparse solutions for
# performance reasons
eval_points, activities, targets = build_linear_system(model, conn, rng=rng)
# account for transform
transform = full_transform(conn)
targets = np.dot(targets, transform.T)
weights, solver_info = conn.solver(activities, targets, rng=rng, E=model.params[conn.post].scaled_encoders.T)
# Synapse type
synapse = conn.synapse
if is_number(synapse):
synapse = ExpSyn(synapse)
# Connect
# TODO: Why is this adjustment of the weights necessary?
weights = weights / synapse.tau / 5.0 * 0.1
connections = [[] for i in range(len(weights))]
for i, cell in enumerate(ens_to_cells[conn.post]):
for j, w in enumerate(weights[:, i]):
if w >= 0.0:
x = np.random.rand()
connections[j].append(synapse.create(cell.neuron.apical(x), w * (x + 1)))
else:
connections[j].append(synapse.create(cell.neuron.soma(0.5), w))
# 3. Add operator creating events for synapses if pre neuron fired
model.add_op(NrnTransmitSpikes(model.sig[conn]["in"], connections))
def build_node_transmission_parameters(model, conn):
"""Build transmission parameters for a connection originating at a Node."""
if conn.pre_obj.output is not None:
# Connection is not from a passthrough Node
# Get the full transform, not including the pre_slice
transform = full_transform(conn, slice_pre=False, allow_scalars=False)
else:
# Connection is from a passthrough Node
# Get the full transform
transform = full_transform(conn, allow_scalars=False)
# If the connection is to neurons and the transform is equivalent in every
# row we treat it as a global inhibition connection and shrink it down to
# one row.
if (isinstance(conn.post_obj, nengo.ensemble.Neurons)
and np.all(transform[0, :] == transform[1:, :])):
# Reduce the size of the transform
transform = np.array([transform[0]])
if conn.pre_obj.output is not None:
return NodeTransmissionParameters(conn.pre_slice, conn.function,
transform)
else:
return PassthroughNodeTransmissionParameters(transform)
def build_node_transmission_parameters(model, conn):
"""Build transmission parameters for a connection originating at a Node."""
if conn.pre_obj.output is not None:
# Connection is not from a passthrough Node
# Get the full transform, not including the pre_slice
transform = full_transform(conn, slice_pre=False, allow_scalars=False)
else:
# Connection is from a passthrough Node
# Get the full transform
transform = full_transform(conn, allow_scalars=False)
# If the connection is to neurons and the transform is equivalent in every
# row we treat it as a global inhibition connection and shrink it down to
# one row.
if (isinstance(conn.post_obj, nengo.ensemble.Neurons) and
np.all(transform[0, :] == transform[1:, :])):
# Reduce the size of the transform
transform = np.array([transform[0]])
if conn.pre_obj.output is not None:
return NodeTransmissionParameters(conn.pre_slice, conn.function,
transform)
else:
return PassthroughNodeTransmissionParameters(transform)
def get_factored_weight_matrices_requirements(network):
(objects, connections) = remove_passthrough_nodes(
*objs_and_connections(network))
# For each Ensemble each incoming connection matrix is N_pre x N big
mem_usage = 0
for ens in [o for o in objects if isinstance(o, nengo.Ensemble)]:
out_conns = [c for c in connections if c.pre is ens and
isinstance(c.post, nengo.Ensemble)]
# Outgoing cost is (n_neurons + 1) x out_d where out_d is the number of
# non-zero rows in the transform matrix
out_transforms = [full_transform(c, allow_scalars=False) for c in
out_conns]
out_dims = sum(np.sum(np.any(np.abs(t) > 0., axis=1)) for t in
out_transforms)
mem_usage += (ens.n_neurons + 1) * out_dims
# Incoming cost is just n_neurons x d
mem_usage += ens.n_neurons * ens.dimensions
return mem_usage * BYTES_PER_ENC_DECODER # (4 bytes per value)
def build_connection(model, conn):
"""
Method to build connections into bioensembles.
Calculates the optimal decoders for this conneciton as though
the presynaptic ensemble was connecting to a hypothetical LIF ensemble.
These decoders are used to calculate the synaptic weights
in init_connection().
Adds a transmit_spike operator for this connection to the model
"""
conn_pre = deref_objview(conn.pre)
conn_post = deref_objview(conn.post)
rng = np.random.RandomState(model.seeds[conn])
if isinstance(conn_pre, nengo.Ensemble) and \
isinstance(conn_pre.neuron_type, BahlNeuron):
# todo: generalize to custom online solvers
if not isinstance(conn.solver, NoSolver) and conn.syn_weights is None:
raise BuildError("Connections from bioneurons must provide a NoSolver or syn_weights"
" (got %s from %s to %s)" % (conn.solver, conn_pre, conn_post))
if (isinstance(conn_post, nengo.Ensemble) and \
isinstance(conn_post.neuron_type, BahlNeuron)):
if not isinstance(conn_pre, nengo.Ensemble) or \
'spikes' not in conn_pre.neuron_type.probeable:
raise BuildError("May only connect spiking neurons (pre=%s) to "
"bioneurons (post=%s)" % (conn_pre, conn_post))
"""
Given a parcicular connection, labeled by conn.pre,
Grab the initial decoders
Generate locations for synapses, then either
(a) Create synapses with weight equal to
w_ij=np.dot(d_i,alpha_j*e_j)/n_syn, where
- d_i is the initial decoder,
- e_j is the single bioneuron encoder
- alpha_j is the single bioneuron gain
- n_syn normalizes total input current for multiple-synapse conns
(b) Load synaptic weights from a prespecified matrix
Add synapses with those weights to bioneuron.synapses,
store this initial synaptic weight matrix in conn.weights = conn.syn_weights
Finally call neuron.init().
"""
if conn.syn_locs is None:
conn.syn_locs = get_synaptic_locations(
rng,
conn_pre.n_neurons,
conn_post.n_neurons,
conn.n_syn)
if conn.syn_weights is None:
use_syn_weights = False
conn.syn_weights = np.zeros((
conn_post.n_neurons,
conn_pre.n_neurons,
conn.syn_locs.shape[2]))
else:
use_syn_weights = True
conn.syn_weights = copy.copy(conn.syn_weights)
if conn.learning_node is not None and hasattr(conn.learning_node, 'syn_encoders'):
# initialize synaptic weights for EncoderNode learned connection
use_syn_weights = True
conn.syn_weights = np.array(conn.learning_node.update_weights())
# Grab decoders from the specified solver (usually nengo.solvers.NoSolver(d))
transform = full_transform(conn, slice_pre=False)
eval_points, decoders, solver_info = build_decoders(
model, conn, rng, transform)
# normalize the area under the ExpSyn curve to compensate for effect of tau
times = np.arange(0, 1.0, 0.001)
k_norm = np.linalg.norm(np.exp((-times/conn.tau_list[0])),1)
# todo: synaptic gains and encoders
# print conn, conn_post.gain, conn.post.encoders
neurons = model.params[conn_post.neurons] # set in build_bioneurons
for j, bahl in enumerate(neurons):
assert isinstance(bahl, Bahl)
loc = conn.syn_locs[j]
encoder = conn_post.encoders[j]
gain = conn_post.gain[j]
bahl.synapses[conn] = np.empty(
(loc.shape[0], loc.shape[1]), dtype=object)
for pre in range(loc.shape[0]):
for syn in range(loc.shape[1]):
if conn.sec == 'apical':
section = bahl.cell.apical(loc[pre, syn])
elif conn.sec == 'tuft':
section = bahl.cell.tuft(loc[pre, syn])
elif conn.sec == 'basal':
section = bahl.cell.basal(loc[pre, syn])
if use_syn_weights: # syn_weights already specified
w_ij = conn.syn_weights[j, pre, syn]
else: # syn_weights should be set by dec_pre and bio encoders/gain
w_ij = np.dot(decoders.T[pre], gain * encoder)
w_ij = w_ij / conn.n_syn / k_norm
conn.syn_weights[j, pre, syn] = w_ij
if conn.syn_type == 'ExpSyn':
tau = conn.tau_list[0]
synapse = ExpSyn(section, w_ij, tau, loc[pre, syn])
elif conn.syn_type == 'Exp2Syn':
assert len(conn.tau_list) == 2, 'Exp2Syn requires tau_rise, tau_fall'
tau1 = conn.tau_list[0]
tau2 = conn.tau_list[1]
synapse = Exp2Syn(section, w_ij, tau1, tau2, loc[pre, syn])
bahl.synapses[conn][pre][syn] = synapse
neuron.init()
model.add_op(TransmitSpikes(
conn, conn_post, conn.learning_node, neurons,
model.sig[conn_pre]['out'], states=[model.time]))
model.params[conn] = BuiltConnection(eval_points=eval_points,
solver_info=solver_info,
transform=transform,
weights=conn.syn_weights)
else: # normal connection
return nengo.builder.connection.build_connection(model, conn)
def split_ensemble_array(self, array, n_parts):
"""
Splits an ensemble array into multiple functionally equivalent ensemble
arrays, removing old connections and probes and adding new ones. Currently
will not split ensemble arrays that have neuron output or input nodes, but
there is no reason this could not be added in the future.
Parameters
----------
array: nengo.EnsembleArray
The array to split
n_parts: int
Number of arrays to split ``array`` into
"""
if array.neuron_input is not None or array.neuron_output is not None:
self.logger.info("Not splitting ensemble array " + array.label + " because it has neuron nodes.")
return
if n_parts < 2:
self.logger.info("Not splitting ensemble array because the " "desired number of parts is < 2.")
return
self.logger.info("+" * 80)
self.logger.info("Splitting ensemble array %s into %d parts.", array.__repr__(), n_parts)
if not isinstance(array, nengo.networks.EnsembleArray):
raise ValueError("'array' must be an EnsembleArray")
inputs, outputs = self.inputs, self.outputs
n_ensembles = array.n_ensembles
D = array.dimensions_per_ensemble
if n_ensembles != len(array.ea_ensembles):
raise ValueError("Number of ensembles does not match")
if len(array.all_connections) != n_ensembles * len(array.all_nodes):
raise ValueError("Number of connections incorrect.")
# assert no extra connections
ea_ensemble_set = set(array.ea_ensembles)
if len(outputs[array.input]) != n_ensembles or (set(c.post for c in outputs[array.input]) != ea_ensemble_set):
raise ValueError("Extra connections from array input")
connection_set = set(array.all_connections)
extra_inputs = set()
extra_outputs = set()
for conn in self.top_level_network.all_connections:
if conn.pre_obj in ea_ensemble_set and conn not in array.all_connections:
extra_outputs.add(conn)
if conn.post_obj in ea_ensemble_set and conn not in array.all_connections:
extra_inputs.add(conn)
for conn in extra_inputs:
self.logger.info("\n" + "*" * 20)
self.logger.info("Extra input connector: %s", conn.pre_obj)
self.logger.info("Synapse: %s", conn.synapse)
self.logger.info("Inputs: ")
for c in inputs[conn.pre_obj]:
self.logger.info(c)
self.logger.info("Outputs: ")
for c in outputs[conn.pre_obj]:
self.logger.info(c)
for conn in extra_outputs:
self.logger.info("\n" + "*" * 20)
self.logger.info("Extra output connector: %s", conn.post_obj)
self.logger.info("Synapse: %s", conn.synapse)
self.logger.info("Inputs: ")
for c in inputs[conn.post_obj]:
self.logger.info(c)
self.logger.info("Outputs: ")
for c in outputs[conn.post_obj]:
self.logger.info(c)
output_nodes = [n for n in array.nodes if n.label[-5:] != "input"]
assert len(output_nodes) > 0
# assert len(filter(lambda x: x.label == 'output', output_nodes)) > 0
for output_node in output_nodes:
extra_connections = set(c.pre for c in inputs[output_node]) != ea_ensemble_set
if extra_connections:
raise ValueError("Extra connections to array output")
sizes = np.zeros(n_parts, dtype=int)
j = 0
for i in range(n_ensembles):
sizes[j] += 1
j = (j + 1) % len(sizes)
indices = np.zeros(len(sizes) + 1, dtype=int)
indices[1:] = np.cumsum(sizes)
self.logger.info("*" * 10 + "Fixing input connections")
# make new input nodes
with array:
new_inputs = [
nengo.Node(size_in=size * D, label="%s%d" % (array.input.label, i)) for i, size in enumerate(sizes)
]
self.node_map[array.input] = new_inputs
# remove connections involving old input node
for conn in array.connections[:]:
if conn.pre_obj is array.input and conn.post in array.ea_ensembles:
array.connections.remove(conn)
# make connections from new input nodes to ensembles
for i, inp in enumerate(new_inputs):
i0, i1 = indices[i], indices[i + 1]
for j, ens in enumerate(array.ea_ensembles[i0:i1]):
with array:
nengo.Connection(inp[j * D : (j + 1) * D], ens, synapse=None)
# make connections into EnsembleArray
for c_in in inputs[array.input]:
# remove connection to old node
self.logger.info("Removing connection from network: %s", c_in)
pre_outputs = outputs[c_in.pre_obj]
transform = full_transform(c_in, slice_pre=False, slice_post=True, allow_scalars=False)
# make connections to new nodes
for i, inp in enumerate(new_inputs):
i0, i1 = indices[i], indices[i + 1]
sub_transform = transform[i0 * D : i1 * D, :]
if self.preserve_zero_conns or np.any(sub_transform):
containing_network = find_object_location(self.top_level_network, c_in)[-1]
assert containing_network, "Connection %s is not in network." % c_in
with containing_network:
new_conn = nengo.Connection(
c_in.pre, inp, synapse=c_in.synapse, function=c_in.function, transform=sub_transform
)
self.logger.info("Added connection: %s", new_conn)
inputs[inp].append(new_conn)
pre_outputs.append(new_conn)
assert remove_from_network(self.top_level_network, c_in)
pre_outputs.remove(c_in)
# remove old input node
array.nodes.remove(array.input)
array.input = None
self.logger.info("*" * 10 + "Fixing output connections")
# loop over outputs
for old_output in output_nodes:
output_sizes = []
for ensemble in array.ensembles:
conn = filter(lambda c: old_output.label in str(c.post), outputs[ensemble])[0]
output_sizes.append(conn.size_out)
# make new output nodes
new_outputs = []
for i in range(n_parts):
i0, i1 = indices[i], indices[i + 1]
i_sizes = output_sizes[i0:i1]
with array:
new_output = nengo.Node(size_in=sum(i_sizes), label="%s_%d" % (old_output.label, i))
new_outputs.append(new_output)
i_inds = np.zeros(len(i_sizes) + 1, dtype=int)
i_inds[1:] = np.cumsum(i_sizes)
# connect ensembles to new output node
for j, e in enumerate(array.ea_ensembles[i0:i1]):
old_conns = [c for c in array.connections if c.pre is e and c.post_obj is old_output]
assert len(old_conns) == 1
old_conn = old_conns[0]
# remove old connection from ensembles
array.connections.remove(old_conn)
# add new connection from ensemble
j0, j1 = i_inds[j], i_inds[j + 1]
with array:
nengo.Connection(
e,
new_output[j0:j1],
synapse=old_conn.synapse,
function=old_conn.function,
transform=old_conn.transform,
)
self.node_map[old_output] = new_outputs
# connect new outputs to external model
output_sizes = [n.size_out for n in new_outputs]
output_inds = np.zeros(len(output_sizes) + 1, dtype=int)
output_inds[1:] = np.cumsum(output_sizes)
for c_out in outputs[old_output]:
assert c_out.function is None
# remove connection to old node
self.logger.info("Removing connection from network: %s", c_out)
transform = full_transform(c_out, slice_pre=True, slice_post=True, allow_scalars=False)
post_inputs = inputs[c_out.post_obj]
# add connections to new nodes
for i, out in enumerate(new_outputs):
i0, i1 = output_inds[i], output_inds[i + 1]
sub_transform = transform[:, i0:i1]
if self.preserve_zero_conns or np.any(sub_transform):
containing_network = find_object_location(self.top_level_network, c_out)[-1]
assert containing_network, "Connection %s is not in network." % c_out
with containing_network:
new_conn = nengo.Connection(out, c_out.post, synapse=c_out.synapse, transform=sub_transform)
self.logger.info("Added connection: %s", new_conn)
outputs[out].append(new_conn)
post_inputs.append(new_conn)
assert remove_from_network(self.top_level_network, c_out)
post_inputs.remove(c_out)
# remove old output node
array.nodes.remove(old_output)
setattr(array, old_output.label, None)
def test_full_transform():
N = 30
with nengo.Network():
neurons3 = nengo.Ensemble(3, dimensions=1).neurons
ens1 = nengo.Ensemble(N, dimensions=1)
ens2 = nengo.Ensemble(N, dimensions=2)
ens3 = nengo.Ensemble(N, dimensions=3)
node1 = nengo.Node(output=[0])
node2 = nengo.Node(output=[0, 0])
node3 = nengo.Node(output=[0, 0, 0])
# Pre slice with default transform -> 1x3 transform
conn = nengo.Connection(node3[2], ens1)
assert np.all(conn.transform == np.array(1))
assert np.all(full_transform(conn) == np.array([[0, 0, 1]]))
# Post slice with 1x1 transform -> 1x2 transform
conn = nengo.Connection(node2[0], ens1, transform=-2)
assert np.all(conn.transform == np.array(-2))
assert np.all(full_transform(conn) == np.array([[-2, 0]]))
# Post slice with 2x1 tranfsorm -> 3x1 transform
conn = nengo.Connection(node1, ens3[::2], transform=[[1], [2]])
assert np.all(conn.transform == np.array([[1], [2]]))
assert np.all(full_transform(conn) == np.array([[1], [0], [2]]))
# Both slices with 2x1 transform -> 3x2 transform
conn = nengo.Connection(ens2[-1], neurons3[1:], transform=[[1], [2]])
assert np.all(conn.transform == np.array([[1], [2]]))
assert np.all(full_transform(conn) == np.array(
[[0, 0], [0, 1], [0, 2]]))
# Full slices that can be optimized away
conn = nengo.Connection(ens3[:], ens3, transform=2)
assert np.all(conn.transform == np.array(2))
assert np.all(full_transform(conn) == np.array(2))
# Pre slice with 1x1 transform on 2x2 slices -> 2x3 transform
conn = nengo.Connection(neurons3[:2], ens2, transform=-1)
assert np.all(conn.transform == np.array(-1))
assert np.all(full_transform(conn) == np.array(
[[-1, 0, 0], [0, -1, 0]]))
# Both slices with 1x1 transform on 2x2 slices -> 3x3 transform
conn = nengo.Connection(neurons3[1:], neurons3[::2], transform=-1)
assert np.all(conn.transform == np.array(-1))
assert np.all(full_transform(conn) == np.array([[0, -1, 0],
[0, 0, 0],
[0, 0, -1]]))
# Both slices with 2x2 transform -> 3x3 transform
conn = nengo.Connection(node3[[0, 2]], neurons3[1:],
transform=[[1, 2], [3, 4]])
assert np.all(conn.transform == np.array([[1, 2], [3, 4]]))
assert np.all(full_transform(conn) == np.array([[0, 0, 0],
[1, 0, 2],
[3, 0, 4]]))
# Both slices with 2x3 transform -> 3x3 transform... IN REVERSE!
conn = nengo.Connection(neurons3[::-1], neurons3[[2, 0]],
transform=[[1, 2, 3], [4, 5, 6]])
assert np.all(conn.transform == np.array([[1, 2, 3], [4, 5, 6]]))
assert np.all(full_transform(conn) == np.array([[6, 5, 4],
[0, 0, 0],
[3, 2, 1]]))
# Both slices using lists
conn = nengo.Connection(neurons3[[1, 0, 2]], neurons3[[2, 1]],
transform=[[1, 2, 3], [4, 5, 6]])
assert np.all(conn.transform == np.array([[1, 2, 3], [4, 5, 6]]))
assert np.all(full_transform(conn) == np.array([[0, 0, 0],
[5, 4, 6],
[2, 1, 3]]))
# using vector
conn = nengo.Connection(ens3[[1, 0, 2]], ens3[[2, 0, 1]],
transform=[1, 2, 3])
assert np.all(conn.transform == np.array([1, 2, 3]))
assert np.all(full_transform(conn) == np.array([[2, 0, 0],
[0, 0, 3],
[0, 1, 0]]))
# using vector and lists
conn = nengo.Connection(ens3[[1, 0, 2]], ens3[[2, 0, 1]],
transform=[1, 2, 3])
assert np.all(conn.transform == np.array([1, 2, 3]))
assert np.all(full_transform(conn) == np.array([[2, 0, 0],
[0, 0, 3],
[0, 1, 0]]))
# using multi-index lists
conn = nengo.Connection(ens3, ens2[[0, 1, 0]])
assert np.all(full_transform(conn) == np.array([[1, 0, 1],
[0, 1, 0]]))
def build_connection(model, conn):
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get input and output connections from pre and post
def get_prepost_signal(is_pre):
target = conn.pre_obj if is_pre else conn.post_obj
key = 'out' if is_pre else 'in'
if target not in model.sig:
raise ValueError("Building %s: the '%s' object %s "
"is not in the model, or has a size of zero."
% (conn, 'pre' if is_pre else 'post', target))
if key not in model.sig[target]:
raise ValueError("Error building %s: the '%s' object %s "
"has a '%s' size of zero." %
(conn, 'pre' if is_pre else 'post', target, key))
return model.sig[target][key]
model.sig[conn]['in'] = get_prepost_signal(is_pre=True)
model.sig[conn]['out'] = get_prepost_signal(is_pre=False)
decoders = None
eval_points = None
solver_info = None
transform = full_transform(conn, slice_pre=False)
# Figure out the signal going across this connection
if (isinstance(conn.pre_obj, Node) or
(isinstance(conn.pre_obj, Ensemble) and
isinstance(conn.pre_obj.neuron_type, Direct))):
# Node or Decoded connection in directmode
if (conn.function is None and isinstance(conn.pre_slice, slice) and
(conn.pre_slice.step is None or conn.pre_slice.step == 1)):
signal = model.sig[conn]['in'][conn.pre_slice]
else:
sig_in, signal = build_pyfunc(
fn=(lambda x: x[conn.pre_slice]) if conn.function is None else
(lambda x: conn.function(x[conn.pre_slice])),
t_in=False,
n_in=model.sig[conn]['in'].size,
n_out=conn.size_mid,
label=str(conn),
model=model)
model.add_op(DotInc(model.sig[conn]['in'],
model.sig['common'][1],
sig_in,
tag="%s input" % conn))
elif isinstance(conn.pre_obj, Ensemble):
# Normal decoded connection
eval_points, activities, targets = build_linear_system(model, conn)
# Use cached solver, if configured
solver = model.decoder_cache.wrap_solver(conn.solver)
if conn.solver.weights:
# account for transform
targets = np.dot(targets, transform.T)
transform = np.array(1., dtype=np.float64)
decoders, solver_info = solver(
activities, targets, rng=rng,
E=model.params[conn.post_obj].scaled_encoders.T)
model.sig[conn]['out'] = model.sig[conn.post_obj.neurons]['in']
signal_size = model.sig[conn]['out'].size
else:
decoders, solver_info = solver(activities, targets, rng=rng)
signal_size = conn.size_mid
# Add operator for decoders
decoders = decoders.T
model.sig[conn]['decoders'] = Signal(
decoders, name="%s.decoders" % conn)
signal = Signal(np.zeros(signal_size), name=str(conn))
model.add_op(Reset(signal))
model.add_op(DotInc(model.sig[conn]['decoders'],
model.sig[conn]['in'],
signal,
tag="%s decoding" % conn))
else:
# Direct connection
signal = model.sig[conn]['in']
# Add operator for filtering
if conn.synapse is not None:
signal = filtered_signal(model, conn, signal, conn.synapse)
if conn.modulatory:
# Make a new signal, effectively detaching from post
model.sig[conn]['out'] = Signal(
np.zeros(model.sig[conn]['out'].size),
name="%s.mod_output" % conn)
model.add_op(Reset(model.sig[conn]['out']))
# Add operator for transform
if isinstance(conn.post_obj, Neurons):
if not model.has_built(conn.post_obj.ensemble):
# Since it hasn't been built, it wasn't added to the Network,
# which is most likely because the Neurons weren't associated
# with an Ensemble.
raise RuntimeError("Connection '%s' refers to Neurons '%s' "
"that are not a part of any Ensemble." % (
conn, conn.post_obj))
if conn.post_slice != slice(None):
raise NotImplementedError(
"Post-slices on connections to neurons are not implemented")
gain = model.params[conn.post_obj.ensemble].gain[conn.post_slice]
if transform.ndim < 2:
transform = transform * gain
else:
transform *= gain[:, np.newaxis]
model.sig[conn]['transform'] = Signal(transform,
name="%s.transform" % conn)
if transform.ndim < 2:
model.add_op(ElementwiseInc(model.sig[conn]['transform'],
signal,
model.sig[conn]['out'],
tag=str(conn)))
else:
model.add_op(DotInc(model.sig[conn]['transform'],
signal,
model.sig[conn]['out'],
tag=str(conn)))
if conn.learning_rule_type:
# Forcing update of signal that is modified by learning rules.
# Learning rules themselves apply DotIncs.
if isinstance(conn.pre_obj, Neurons):
modified_signal = model.sig[conn]['transform']
elif isinstance(conn.pre_obj, Ensemble):
if conn.solver.weights:
# TODO: make less hacky.
# Have to do this because when a weight_solver
# is provided, then learning rules should operators on
# "decoders" which is really the weight matrix.
model.sig[conn]['transform'] = model.sig[conn]['decoders']
modified_signal = model.sig[conn]['transform']
else:
modified_signal = model.sig[conn]['decoders']
else:
raise TypeError("Can't apply learning rules to connections of "
"this type. pre type: %s, post type: %s"
% (type(conn.pre_obj).__name__,
type(conn.post_obj).__name__))
model.add_op(PreserveValue(modified_signal))
model.params[conn] = BuiltConnection(decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info)
def create_replacement_connection(c_in, c_out):
"""Generate a new Connection to replace two through a passthrough Node"""
assert c_in.post_obj is c_out.pre_obj
assert c_in.post_obj.output is None
# determine the filter for the new Connection
if c_in.synapse is None:
synapse = c_out.synapse
elif c_out.synapse is None:
synapse = c_in.synapse
else:
raise NotImplementedError('Cannot merge two filters')
# Note: the algorithm below is in the right ballpark,
# but isn't exactly the same as two low-pass filters
# filter = c_out.filter + c_in.filter
function = c_in.function
if c_out.function is not None:
raise Exception('Cannot remove a Node with a '
'function being computed on it')
# compute the combined transform
transform = np.dot(full_transform(c_out), full_transform(c_in))
# check if the transform is 0 (this happens a lot
# with things like identity transforms)
if np.all(transform == 0):
return None
# Determine the combined keyspace
if (getattr(c_out, 'keyspace', None) is not None and
getattr(c_in, 'keyspace', None) is None):
# If the out keyspace is specified but the IN isn't, then use the out
# keyspace
keyspace = getattr(c_out, 'keyspace', None)
elif (getattr(c_in, 'keyspace', None) is not None and
getattr(c_out, 'keyspace', None) is None):
# Vice versa
keyspace = getattr(c_in, 'keyspace', None)
elif getattr(c_in, 'keyspace', None) == getattr(c_out, 'keyspace', None):
# The keyspaces are equivalent
keyspace = getattr(c_in, 'keyspace', None)
else:
# XXX: The incoming and outcoming connections have assigned
# keyspaces, this shouldn't occur (often if not at all).
raise NotImplementedError('Cannot merge two keyspaces.')
# Determine the type of connection to use
if c_in.__class__ is c_out.__class__:
# Types are equivalent, so use this type
ctype = c_in.__class__
elif c_in.__class__ is IntermediateConnection:
# In type is default, use out type
ctype = c_out.__class__
elif c_out.__class__ is IntermediateConnection:
# Out type is default, use in type
ctype = c_in.__class__
else:
raise NotImplementedError("Cannot merge '%s' and '%s' connection "
"types." % (c_in.__class__,
c_out.__class__))
if ctype is nengo.Connection:
ctype = IntermediateConnection
c = ctype(c_in.pre_obj, c_out.post_obj, synapse=synapse,
transform=transform, function=function, keyspace=keyspace)
return c
def build_connection(model, conn):
is_ens = isinstance(conn.post_obj, nengo.Ensemble)
is_neurons = isinstance(conn.post_obj, nengo.ensemble.Neurons)
is_NEURON = False
if is_ens:
if isinstance(conn.post_obj.neuron_type, BioNeuron):
is_NEURON = True
else:
is_NEURON = False
elif is_neurons:
if isinstance(conn.post_obj.ensemble.neuron_type, BioNeuron):
is_NEURON = True
else:
is_ens = False
is_neurons = False
is_NEURON = False
if is_NEURON:
model.sig[conn]['in'] = model.sig[conn.pre_obj]['out']
assert isinstance(
conn.pre_obj,
nengo.Ensemble) and 'spikes' in conn.pre_obj.neuron_type.probeable
post_obj = conn.post_obj if is_ens else conn.post_obj.ensemble
pre_obj = conn.pre_obj
if isinstance(conn.synapse, AMPA):
taus = "AMPA"
elif isinstance(conn.synapse, GABA):
taus = "GABA"
elif isinstance(conn.synapse, NMDA):
taus = "NMDA"
elif isinstance(conn.synapse, LinearSystem):
taus = -1.0 / np.array(conn.synapse.poles) * 1000 # convert to ms
else:
raise "synapse type %s not understood (connection %s)" % (
conn.synapse, conn)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
conn.rng = np.random.RandomState(model.seeds[conn])
# conn.e = conn.rng.uniform(-1, 1, size=(pre_obj.n_neurons, post_obj.n_neurons, post_obj.dimensions))
conn.e = np.zeros(
(pre_obj.n_neurons, post_obj.n_neurons, post_obj.dimensions))
conn.locations = conn.rng.uniform(0,
1,
size=(pre_obj.n_neurons,
post_obj.n_neurons))
if post_obj.neuron_type.cell_type == "Pyramidal":
conn.compartments = conn.rng.randint(
0, 3,
size=(pre_obj.n_neurons,
post_obj.n_neurons)) # 0=proximal, 1=distal, 2=basal
elif post_obj.neuron_type.cell_type == "Interneuron":
conn.compartments = np.zeros(
shape=(pre_obj.n_neurons,
post_obj.n_neurons)) # 0=dendrite
conn.synapses = np.zeros((pre_obj.n_neurons, post_obj.n_neurons),
dtype=list)
conn.netcons = np.zeros((pre_obj.n_neurons, post_obj.n_neurons),
dtype=list)
conn.weights = np.zeros((pre_obj.n_neurons, post_obj.n_neurons))
conn.v_recs = []
transform = full_transform(conn, slice_pre=False)
eval_points, d, solver_info = model.build(conn.solver, conn,
conn.rng, transform)
conn.d = d.T
conn.transmitspike = None
for post in range(post_obj.n_neurons):
nrn = model.params[post_obj.neurons][post]
for pre in range(pre_obj.n_neurons):
conn.weights[pre, post] = np.dot(conn.d[pre], conn.e[pre,
post])
reversal = 0.0 if conn.weights[pre, post] > 0 else -70.0
if conn.compartments[pre, post] == 0:
if post_obj.neuron_type.cell_type == "Pyramidal":
loc = nrn.prox(conn.locations[pre, post])
elif post_obj.neuron_type.cell_type == "Interneuron":
loc = nrn.dendrite(conn.locations[pre, post])
elif conn.compartments[pre, post] == 1:
loc = nrn.dist(conn.locations[pre, post])
else:
loc = nrn.basal(conn.locations[pre, post])
if type(taus) == str:
if taus == "AMPA":
syn = neuron.h.ampa(loc)
elif taus == "GABA":
syn = neuron.h.gaba(loc)
elif taus == "NMDA":
syn = neuron.h.nmda(loc)
else:
raise "synapse %s not understood" % taus
elif len(taus) == 1:
syn = neuron.h.ExpSyn(loc)
syn.tau = taus[0]
syn.e = reversal
elif len(taus) == 2:
# syn = neuron.h.Exp2Syn(loc)
syn = neuron.h.doubleexp(loc)
syn.tauRise = np.min(taus)
syn.tauFall = np.max(taus)
syn.e = reversal
conn.synapses[pre, post] = syn
conn.netcons[pre,
post] = neuron.h.NetCon(None, conn.synapses[pre,
post])
conn.netcons[pre, post].weight[0] = np.abs(conn.weights[pre,
post])
conn.v_recs.append(neuron.h.Vector())
conn.v_recs[post].record(nrn.soma(0.5)._ref_v)
transmitspike = TransmitSpikes(model.params[post_obj.neurons],
conn.netcons,
model.sig[conn.pre_obj]['out'],
DA=post_obj.neuron_type.DA,
states=[model.time],
dt=model.dt)
model.add_op(transmitspike)
conn.transmitspike = transmitspike
model.params[conn] = BuiltConnection(eval_points=eval_points,
solver_info=solver_info,
transform=transform,
weights=d)
else:
c = nengo.builder.connection.build_connection(model, conn)
model.sig[conn]['weights'].readonly = False
return c
def test_full_transform():
"""Tests ``full_transform`` and its exceptions"""
N = 30
with nengo.Network():
neurons3 = nengo.Ensemble(3, dimensions=1).neurons
ens1 = nengo.Ensemble(N, dimensions=1)
ens2 = nengo.Ensemble(N, dimensions=2)
ens3 = nengo.Ensemble(N, dimensions=3)
node1 = nengo.Node(output=[0])
node2 = nengo.Node(output=[0, 0])
node3 = nengo.Node(output=[0, 0, 0])
# error for non-Dense transform
conn = nengo.Connection(
ens2, ens3, transform=nengo.transforms.Sparse((3, 2), indices=[(0, 0)])
)
with pytest.raises(ValidationError, match="can only be applied to Dense"):
full_transform(conn)
# Pre slice with default transform -> 1x3 transform
conn = nengo.Connection(node3[2], ens1)
assert isinstance(conn.transform, NoTransform)
assert np.all(full_transform(conn) == np.array([[0, 0, 1]]))
# Post slice with 1x1 transform -> 1x2 transform
conn = nengo.Connection(node2[0], ens1, transform=-2)
assert np.all(conn.transform.init == np.array(-2))
assert np.all(full_transform(conn) == np.array([[-2, 0]]))
# Post slice with 2x1 tranfsorm -> 3x1 transform
conn = nengo.Connection(node1, ens3[::2], transform=[[1], [2]])
assert np.all(conn.transform.init == np.array([[1], [2]]))
assert np.all(full_transform(conn) == np.array([[1], [0], [2]]))
# Both slices with 2x1 transform -> 3x2 transform
conn = nengo.Connection(ens2[-1], neurons3[1:], transform=[[1], [2]])
assert np.all(conn.transform.init == np.array([[1], [2]]))
assert np.all(full_transform(conn) == np.array([[0, 0], [0, 1], [0, 2]]))
# Full slices that can be optimized away
conn = nengo.Connection(ens3[:], ens3, transform=2)
assert np.all(conn.transform.init == np.array(2))
assert np.all(full_transform(conn) == np.array(2))
# Pre slice with 1x1 transform on 2x2 slices -> 2x3 transform
conn = nengo.Connection(neurons3[:2], ens2, transform=-1)
assert np.all(conn.transform.init == np.array(-1))
assert np.all(full_transform(conn) == np.array([[-1, 0, 0], [0, -1, 0]]))
# Both slices with 1x1 transform on 2x2 slices -> 3x3 transform
conn = nengo.Connection(neurons3[1:], neurons3[::2], transform=-1)
assert np.all(conn.transform.init == np.array(-1))
assert np.all(
full_transform(conn) == np.array([[0, -1, 0], [0, 0, 0], [0, 0, -1]])
)
# Both slices with 2x2 transform -> 3x3 transform
conn = nengo.Connection(node3[[0, 2]], neurons3[1:], transform=[[1, 2], [3, 4]])
assert np.all(conn.transform.init == np.array([[1, 2], [3, 4]]))
assert np.all(
full_transform(conn) == np.array([[0, 0, 0], [1, 0, 2], [3, 0, 4]])
)
# Both slices with 2x3 transform -> 3x3 transform... IN REVERSE!
conn = nengo.Connection(
neurons3[::-1], neurons3[[2, 0]], transform=[[1, 2, 3], [4, 5, 6]]
)
assert np.all(conn.transform.init == np.array([[1, 2, 3], [4, 5, 6]]))
assert np.all(
full_transform(conn) == np.array([[6, 5, 4], [0, 0, 0], [3, 2, 1]])
)
# Both slices using lists
conn = nengo.Connection(
neurons3[[1, 0, 2]], neurons3[[2, 1]], transform=[[1, 2, 3], [4, 5, 6]]
)
assert np.all(conn.transform.init == np.array([[1, 2, 3], [4, 5, 6]]))
assert np.all(
full_transform(conn) == np.array([[0, 0, 0], [5, 4, 6], [2, 1, 3]])
)
# using vector
conn = nengo.Connection(ens3[[1, 0, 2]], ens3[[2, 0, 1]], transform=[1, 2, 3])
assert np.all(conn.transform.init == np.array([1, 2, 3]))
assert np.all(
full_transform(conn) == np.array([[2, 0, 0], [0, 0, 3], [0, 1, 0]])
)
# using vector 1D
conn = nengo.Connection(ens1, ens1, transform=[5])
assert full_transform(conn).ndim != 1
assert np.all(full_transform(conn) == 5)
# using vector and lists
conn = nengo.Connection(ens3[[1, 0, 2]], ens3[[2, 0, 1]], transform=[1, 2, 3])
assert np.all(conn.transform.init == np.array([1, 2, 3]))
assert np.all(
full_transform(conn) == np.array([[2, 0, 0], [0, 0, 3], [0, 1, 0]])
)
# using multi-index lists
conn = nengo.Connection(ens3, ens2[[0, 1, 0]])
assert np.all(full_transform(conn) == np.array([[1, 0, 1], [0, 1, 0]]))
def build_connection(model, conn):
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get input and output connections from pre and post
def get_prepost_signal(is_pre):
target = conn.pre_obj if is_pre else conn.post_obj
print('pre', conn.pre_obj, 'post', conn.post_obj)
key = 'out' if is_pre else 'in'
if target not in model.sig:
raise ValueError("Building %s: the '%s' object %s "
"is not in the model, or has a size of zero." %
(conn, 'pre' if is_pre else 'post', target))
if key not in model.sig[target]:
raise ValueError("Error building %s: the '%s' object %s "
"has a '%s' size of zero." %
(conn, 'pre' if is_pre else 'post', target, key))
return model.sig[target][key]
model.sig[conn]['in'] = get_prepost_signal(is_pre=True)
model.sig[conn]['out'] = get_prepost_signal(is_pre=False)
decoders = None
eval_points = None
solver_info = None
transform = full_transform(conn, slice_pre=False)
# Figure out the signal going across this connection
if (isinstance(conn.pre_obj, Node)
or (isinstance(conn.pre_obj, Ensemble)
and isinstance(conn.pre_obj.neuron_type, Direct))):
# Node or Decoded connection in directmode
if (conn.function is None and isinstance(conn.pre_slice, slice)
and (conn.pre_slice.step is None or conn.pre_slice.step == 1)):
signal = model.sig[conn]['in'][conn.pre_slice]
else:
sig_in, signal = build_pyfunc(
fn=(lambda x: x[conn.pre_slice]) if conn.function is None else
(lambda x: conn.function(x[conn.pre_slice])),
t_in=False,
n_in=model.sig[conn]['in'].size,
n_out=conn.size_mid,
label=str(conn),
model=model)
model.add_op(
DotInc(model.sig[conn]['in'],
model.sig['common'][1],
sig_in,
tag="%s input" % conn))
elif isinstance(conn.pre_obj, Ensemble):
# Normal decoded connection
eval_points, activities, targets = build_linear_system(
model, conn, rng)
# Use cached solver, if configured
solver = model.decoder_cache.wrap_solver(conn.solver)
if conn.solver.weights:
# account for transform
targets = np.dot(targets, transform.T)
transform = np.array(1, dtype=rc.get('precision', 'dtype'))
decoders, solver_info = solver(
activities,
targets,
rng=rng,
E=model.params[conn.post_obj].scaled_encoders.T)
model.sig[conn]['out'] = model.sig[conn.post_obj.neurons]['in']
signal_size = model.sig[conn]['out'].size
else:
decoders, solver_info = solver(activities, targets, rng=rng)
signal_size = conn.size_mid
# Add operator for decoders
decoders = decoders.T
model.sig[conn]['decoders'] = model.Signal(decoders,
name="%s.decoders" % conn)
signal = model.Signal(npext.castDecimal(np.zeros(signal_size)),
name=str(conn))
model.add_op(Reset(signal))
model.add_op(
DotInc(model.sig[conn]['decoders'],
model.sig[conn]['in'],
signal,
tag="%s decoding" % conn))
else:
# Direct connection
signal = model.sig[conn]['in']
# Add operator for filtering
if conn.synapse is not None:
signal = filtered_signal(model, conn, signal, conn.synapse)
if conn.modulatory:
# Make a new signal, effectively detaching from post
model.sig[conn]['out'] = model.Signal(npext.castDecimal(
np.zeros(model.sig[conn]['out'].size)),
name="%s.mod_output" % conn)
model.add_op(Reset(model.sig[conn]['out']))
# Add operator for transform
if isinstance(conn.post_obj, Neurons):
if not model.has_built(conn.post_obj.ensemble):
# Since it hasn't been built, it wasn't added to the Network,
# which is most likely because the Neurons weren't associated
# with an Ensemble.
raise RuntimeError("Connection '%s' refers to Neurons '%s' "
"that are not a part of any Ensemble." %
(conn, conn.post_obj))
if conn.post_slice != slice(None):
raise NotImplementedError(
"Post-slices on connections to neurons are not implemented")
gain = model.params[conn.post_obj.ensemble].gain[conn.post_slice]
if transform.ndim < 2:
transform = transform * gain
else:
transform *= gain[:, np.newaxis]
model.sig[conn]['transform'] = model.Signal(transform,
name="%s.transform" % conn)
print('abcd', model.sig[conn]['out'].value, signal.value)
if transform.ndim < 2:
print('line 174', model.sig[conn]['transform'].value)
model.add_op(
ElementwiseInc(model.sig[conn]['transform'],
signal,
model.sig[conn]['out'],
tag=str(conn)))
else:
model.add_op(
DotInc(model.sig[conn]['transform'],
signal,
model.sig[conn]['out'],
tag=str(conn)))
if conn.learning_rule_type:
# Forcing update of signal that is modified by learning rules.
# Learning rules themselves apply DotIncs.
if isinstance(conn.pre_obj, Neurons):
modified_signal = model.sig[conn]['transform']
elif isinstance(conn.pre_obj, Ensemble):
if conn.solver.weights:
# TODO: make less hacky.
# Have to do this because when a weight_solver
# is provided, then learning rules should operators on
# "decoders" which is really the weight matrix.
model.sig[conn]['transform'] = model.sig[conn]['decoders']
modified_signal = model.sig[conn]['transform']
else:
modified_signal = model.sig[conn]['decoders']
else:
raise TypeError(
"Can't apply learning rules to connections of "
"this type. pre type: %s, post type: %s" %
(type(conn.pre_obj).__name__, type(conn.post_obj).__name__))
model.add_op(PreserveValue(modified_signal))
model.params[conn] = BuiltConnection(decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info)
def test_full_transform():
N = 30
with nengo.Network():
neurons3 = nengo.Ensemble(3, dimensions=1).neurons
ens1 = nengo.Ensemble(N, dimensions=1)
ens2 = nengo.Ensemble(N, dimensions=2)
ens3 = nengo.Ensemble(N, dimensions=3)
node1 = nengo.Node(output=[0])
node2 = nengo.Node(output=[0, 0])
node3 = nengo.Node(output=[0, 0, 0])
# Pre slice with default transform -> 1x3 transform
conn = nengo.Connection(node3[2], ens1)
assert np.all(conn.transform.init == np.array(1))
assert np.all(full_transform(conn) == np.array([[0, 0, 1]]))
# Post slice with 1x1 transform -> 1x2 transform
conn = nengo.Connection(node2[0], ens1, transform=-2)
assert np.all(conn.transform.init == np.array(-2))
assert np.all(full_transform(conn) == np.array([[-2, 0]]))
# Post slice with 2x1 tranfsorm -> 3x1 transform
conn = nengo.Connection(node1, ens3[::2], transform=[[1], [2]])
assert np.all(conn.transform.init == np.array([[1], [2]]))
assert np.all(full_transform(conn) == np.array([[1], [0], [2]]))
# Both slices with 2x1 transform -> 3x2 transform
conn = nengo.Connection(ens2[-1], neurons3[1:], transform=[[1], [2]])
assert np.all(conn.transform.init == np.array([[1], [2]]))
assert np.all(
full_transform(conn) == np.array([[0, 0], [0, 1], [0, 2]]))
# Full slices that can be optimized away
conn = nengo.Connection(ens3[:], ens3, transform=2)
assert np.all(conn.transform.init == np.array(2))
assert np.all(full_transform(conn) == np.array(2))
# Pre slice with 1x1 transform on 2x2 slices -> 2x3 transform
conn = nengo.Connection(neurons3[:2], ens2, transform=-1)
assert np.all(conn.transform.init == np.array(-1))
assert np.all(
full_transform(conn) == np.array([[-1, 0, 0], [0, -1, 0]]))
# Both slices with 1x1 transform on 2x2 slices -> 3x3 transform
conn = nengo.Connection(neurons3[1:], neurons3[::2], transform=-1)
assert np.all(conn.transform.init == np.array(-1))
assert np.all(
full_transform(conn) == np.array([[0, -1, 0], [0, 0, 0],
[0, 0, -1]]))
# Both slices with 2x2 transform -> 3x3 transform
conn = nengo.Connection(node3[[0, 2]],
neurons3[1:],
transform=[[1, 2], [3, 4]])
assert np.all(conn.transform.init == np.array([[1, 2], [3, 4]]))
assert np.all(
full_transform(conn) == np.array([[0, 0, 0], [1, 0, 2], [3, 0, 4]
]))
# Both slices with 2x3 transform -> 3x3 transform... IN REVERSE!
conn = nengo.Connection(neurons3[::-1],
neurons3[[2, 0]],
transform=[[1, 2, 3], [4, 5, 6]])
assert np.all(conn.transform.init == np.array([[1, 2, 3], [4, 5, 6]]))
assert np.all(
full_transform(conn) == np.array([[6, 5, 4], [0, 0, 0], [3, 2, 1]
]))
# Both slices using lists
conn = nengo.Connection(neurons3[[1, 0, 2]],
neurons3[[2, 1]],
transform=[[1, 2, 3], [4, 5, 6]])
assert np.all(conn.transform.init == np.array([[1, 2, 3], [4, 5, 6]]))
assert np.all(
full_transform(conn) == np.array([[0, 0, 0], [5, 4, 6], [2, 1, 3]
]))
# using vector
conn = nengo.Connection(ens3[[1, 0, 2]],
ens3[[2, 0, 1]],
transform=[1, 2, 3])
assert np.all(conn.transform.init == np.array([1, 2, 3]))
assert np.all(
full_transform(conn) == np.array([[2, 0, 0], [0, 0, 3], [0, 1, 0]
]))
# using vector 1D
conn = nengo.Connection(ens1, ens1, transform=[5])
assert full_transform(conn).ndim != 1
assert np.all(full_transform(conn) == 5)
# using vector and lists
conn = nengo.Connection(ens3[[1, 0, 2]],
ens3[[2, 0, 1]],
transform=[1, 2, 3])
assert np.all(conn.transform.init == np.array([1, 2, 3]))
assert np.all(
full_transform(conn) == np.array([[2, 0, 0], [0, 0, 3], [0, 1, 0]
]))
# using multi-index lists
conn = nengo.Connection(ens3, ens2[[0, 1, 0]])
assert np.all(full_transform(conn) == np.array([[1, 0, 1], [0, 1, 0]]))
def build_connection(model, conn):
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get input and output connections from pre and post
def get_prepost_signal(is_pre):
target = conn.pre_obj if is_pre else conn.post_obj
key = 'out' if is_pre else 'in'
if target not in model.sig:
raise ValueError("Building %s: the '%s' object %s "
"is not in the model, or has a size of zero." %
(conn, 'pre' if is_pre else 'post', target))
if key not in model.sig[target]:
raise ValueError("Error building %s: the '%s' object %s "
"has a '%s' size of zero." %
(conn, 'pre' if is_pre else 'post', target, key))
return model.sig[target][key]
model.sig[conn]['in'] = get_prepost_signal(is_pre=True)
model.sig[conn]['out'] = get_prepost_signal(is_pre=False)
decoders = None
eval_points = None
solver_info = None
transform = full_transform(conn, slice_pre=False)
# Figure out the signal going across this connection
if (isinstance(conn.pre_obj, Node)
or (isinstance(conn.pre_obj, Ensemble)
and isinstance(conn.pre_obj.neuron_type, Direct))):
# Node or Decoded connection in directmode
if (conn.function is None and isinstance(conn.pre_slice, slice)
and (conn.pre_slice.step is None or conn.pre_slice.step == 1)):
signal = model.sig[conn]['in'][conn.pre_slice]
else:
signal = Signal(np.zeros(conn.size_mid), name='%s.func' % conn)
fn = ((lambda x: x[conn.pre_slice]) if conn.function is None else
(lambda x: conn.function(x[conn.pre_slice])))
model.add_op(
SimPyFunc(output=signal,
fn=fn,
t_in=False,
x=model.sig[conn]['in']))
elif isinstance(conn.pre_obj, Ensemble):
# Normal decoded connection
eval_points, activities, targets = build_linear_system(
model, conn, rng)
# Use cached solver, if configured
solver = model.decoder_cache.wrap_solver(conn.solver)
if conn.solver.weights:
# include transform in solved weights
targets = np.dot(targets, transform.T)
transform = np.array(1., dtype=np.float64)
decoders, solver_info = solver(
activities,
targets,
rng=rng,
E=model.params[conn.post_obj].scaled_encoders.T)
model.sig[conn]['out'] = model.sig[conn.post_obj.neurons]['in']
signal_size = model.sig[conn]['out'].size
else:
decoders, solver_info = solver(activities, targets, rng=rng)
signal_size = conn.size_mid
# Add operator for decoders
decoders = decoders.T
model.sig[conn]['decoders'] = Signal(decoders,
name="%s.decoders" % conn)
signal = Signal(np.zeros(signal_size), name=str(conn))
model.add_op(Reset(signal))
model.add_op(
DotInc(model.sig[conn]['decoders'],
model.sig[conn]['in'],
signal,
tag="%s decoding" % conn))
else:
# Direct connection
signal = model.sig[conn]['in']
# Add operator for filtering
if conn.synapse is not None:
signal = filtered_signal(model, conn, signal, conn.synapse)
# Add operator for transform
if isinstance(conn.post_obj, Neurons):
if not model.has_built(conn.post_obj.ensemble):
# Since it hasn't been built, it wasn't added to the Network,
# which is most likely because the Neurons weren't associated
# with an Ensemble.
raise RuntimeError("Connection '%s' refers to Neurons '%s' "
"that are not a part of any Ensemble." %
(conn, conn.post_obj))
if conn.post_slice != slice(None):
raise NotImplementedError(
"Post-slices on connections to neurons are not implemented")
gain = model.params[conn.post_obj.ensemble].gain[conn.post_slice]
if transform.ndim < 2:
transform = transform * gain
else:
transform *= gain[:, np.newaxis]
model.sig[conn]['transform'] = Signal(transform,
name="%s.transform" % conn)
if transform.ndim < 2:
model.add_op(
ElementwiseInc(model.sig[conn]['transform'],
signal,
model.sig[conn]['out'],
tag=str(conn)))
else:
model.add_op(
DotInc(model.sig[conn]['transform'],
signal,
model.sig[conn]['out'],
tag=str(conn)))
# Build learning rules
if conn.learning_rule:
if isinstance(conn.pre_obj, Ensemble):
model.add_op(PreserveValue(model.sig[conn]['decoders']))
else:
model.add_op(PreserveValue(model.sig[conn]['transform']))
if isinstance(conn.pre_obj, Ensemble) and conn.solver.weights:
# TODO: make less hacky.
# Have to do this because when a weight_solver
# is provided, then learning rules should operate on
# "decoders" which is really the weight matrix.
model.sig[conn]['transform'] = model.sig[conn]['decoders']
rule = conn.learning_rule
if is_iterable(rule):
for r in itervalues(rule) if isinstance(rule, dict) else rule:
model.build(r)
elif rule is not None:
model.build(rule)
model.params[conn] = BuiltConnection(decoders=decoders,
eval_points=eval_points,
transform=transform,
solver_info=solver_info)
def build_nrn_connection(model, conn):
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
# Check pre-conditions
assert isinstance(conn.pre, nengo.Ensemble)
assert not isinstance(conn.pre.neuron_type, nengo.neurons.Direct)
# FIXME assert no rate neurons are used. How to do that?
# Get input signal
# FIXME this should probably be
# model.sig[conn]['in'] = model.sig[conn.pre]["out"]
# in both cases
if isinstance(conn.pre, nengo.ensemble.Neurons):
model.sig[conn]['in'] = model.sig[conn.pre.ensemble]['out']
else:
model.sig[conn]['in'] = model.sig[conn.pre]["out"]
# Figure out type of connection
if isinstance(conn.post, nengo.ensemble.Neurons):
raise NotImplementedError() # TODO
elif isinstance(conn.post.neuron_type, Compartmental):
pass
else:
raise AssertionError(
"This function should only be called if post neurons are "
"compartmental.")
# Solve for weights
# FIXME just assuming solver is a weight solver, may that break?
# Default solver should probably also produce sparse solutions for
# performance reasons
eval_points, activities, targets = build_linear_system(model,
conn,
rng=rng)
# account for transform
transform = full_transform(conn)
targets = np.dot(targets, transform.T)
weights, solver_info = conn.solver(
activities,
targets,
rng=rng,
E=model.params[conn.post].scaled_encoders.T)
# Synapse type
synapse = conn.synapse
if is_number(synapse):
synapse = ExpSyn(synapse)
# Connect
# TODO: Why is this adjustment of the weights necessary?
weights = weights / synapse.tau / 5. * .1
connections = [[] for i in range(len(weights))]
for i, cell in enumerate(ens_to_cells[conn.post]):
for j, w in enumerate(weights[:, i]):
if w >= 0.0:
x = np.random.rand()
connections[j].append(
synapse.create(cell.neuron.apical(x), w * (x + 1)))
else:
connections[j].append(synapse.create(cell.neuron.soma(0.5), w))
# 3. Add operator creating events for synapses if pre neuron fired
model.add_op(NrnTransmitSpikes(model.sig[conn]['in'], connections))
