def __init__(self,
dimensions,
n_neurons_per_ensemble=50,
mutual_inhib=1.0,
threshold=0.0,
**kwargs):
if "net" in kwargs:
raise ObsoleteError("The 'net' argument is no longer supported.")
kwargs.setdefault("label", "Thalamus")
super().__init__(**kwargs)
with self:
self.actions = EnsembleArray(
n_neurons_per_ensemble,
dimensions,
intercepts=Uniform(threshold, 1),
encoders=Choice([[1.0]]),
label="actions",
)
Connection(
self.actions.output,
self.actions.input,
transform=(np.eye(dimensions) - 1) * mutual_inhib,
)
self.bias = Node([1], label="thalamus bias")
Connection(self.bias,
self.actions.input,
transform=np.ones((dimensions, 1)))
self.input = self.actions.input
self.output = self.actions.output
def add_default_output_vector(
self,
output_vector,
output_name="output",
n_neurons=50,
min_activation_value=0.5,
):
"""Adds a default output vector to the associative memory network.
The default output vector is chosen if the input matches none of
the given input vectors.
Parameters
----------
output_vector: array_like
The vector to be produced if the input value matches none of
the vectors in the input vector list.
output_name: str, optional
The name of the input to which the default output vector
should be applied.
n_neurons: int, optional
Number of neurons to use for the default output vector ensemble.
min_activation_value: float, optional
Minimum activation value (i.e. threshold) to use to disable
the default output vector.
"""
with self.default_ens_config:
default_vector_ens = Ensemble(
n_neurons, 1, label=f"Default {output_name} vector")
Connection(self.bias_node, default_vector_ens, synapse=None)
tr = -(1.0 / min_activation_value) * np.ones((1, self.n_items))
if self.thresh_ens is not None:
c = Connection(self.thresh_ens.output,
default_vector_ens,
transform=tr)
else:
c = Connection(self.elem_output,
default_vector_ens,
transform=tr)
# Add the output connection to the output connection list
self.default_vector_inhibit_conns.append(c)
# Make new output class attribute and connect to it
output = getattr(self, output_name)
Connection(
default_vector_ens,
output,
transform=np.array(output_vector, ndmin=2).T,
synapse=None,
)
if self.inhibit is not None:
Connection(
self.inhibit,
default_vector_ens,
transform=-self._inhib_scale,
synapse=None,
)
def __init__(self, recurrent_tau, n_neurons, dimensions, **kwargs):
if "net" in kwargs:
raise ObsoleteError("The 'net' argument is no longer supported.")
kwargs.setdefault("label", "Integrator")
super().__init__(**kwargs)
with self:
self.input = Node(size_in=dimensions)
self.ensemble = Ensemble(n_neurons, dimensions=dimensions)
Connection(self.ensemble, self.ensemble, synapse=recurrent_tau)
Connection(self.input, self.ensemble, transform=recurrent_tau, synapse=None)
self.output = self.ensemble
def add_neuron_output(self):
"""Adds a node that collects the neural output of all ensembles.
Direct neuron output is useful for plotting the spike raster of
all neurons in the ensemble array.
This node is accessible through the 'neuron_output' attribute
of this ensemble array.
"""
if self.neuron_output is not None:
warnings.warn("neuron_output already exists. Returning.")
return self.neuron_output
if isinstance(self.ea_ensembles[0].neuron_type, Direct):
raise ValidationError(
"Ensembles use Direct neuron type. "
"Cannot get neuron output from Direct neurons.",
attr="ea_ensembles[0].neuron_type",
obj=self,
)
self.neuron_output = Node(
size_in=self.n_neurons_per_ensemble * self.n_ensembles,
label="neuron_output",
)
for i, ens in enumerate(self.ea_ensembles):
Connection(
ens.neurons,
self.neuron_output[i * self.n_neurons_per_ensemble:(i + 1) *
self.n_neurons_per_ensemble],
synapse=None,
)
return self.neuron_output
def conn_probe(model, probe):
"""Build a "connection" probe type.
Connection probes create a connection from the target, and probe
the resulting signal (used when you want to probe the default
output of an object, which may not have a predefined signal).
"""
conn = Connection(
probe.target,
probe,
synapse=probe.synapse,
solver=probe.solver,
add_to_container=False,
)
# Set connection's seed to probe's (which isn't used elsewhere)
model.seeded[conn] = model.seeded[probe]
model.seeds[conn] = model.seeds[probe]
# Make a sink signal for the connection
model.sig[probe]["in"] = Signal(shape=conn.size_out, name=str(probe))
model.add_op(Reset(model.sig[probe]["in"]))
# Build the connection
model.build(conn)
def add_neuron_input(self):
"""Adds a node that provides input to the neurons of all ensembles.
Direct neuron input is useful for inhibiting the activity of all
neurons in the ensemble array.
This node is accessible through the 'neuron_input' attribute
of this ensemble array.
"""
if self.neuron_input is not None:
warnings.warn("neuron_input already exists. Returning.")
return self.neuron_input
if isinstance(self.ea_ensembles[0].neuron_type, Direct):
raise ValidationError(
"Ensembles use Direct neuron type. "
"Cannot give neuron input to Direct neurons.",
attr="ea_ensembles[0].neuron_type",
obj=self,
)
self.neuron_input = Node(size_in=self.n_neurons_per_ensemble *
self.n_ensembles,
label="neuron_input")
for i, ens in enumerate(self.ea_ensembles):
Connection(
self.neuron_input[i * self.n_neurons_per_ensemble:(i + 1) *
self.n_neurons_per_ensemble],
ens.neurons,
synapse=None,
)
return self.neuron_input
def __init__(self, recurrent_tau, frequency, n_neurons, **kwargs):
if "net" in kwargs:
raise ObsoleteError("The 'net' argument is no longer supported.")
kwargs.setdefault("label", "Oscillator")
super().__init__(**kwargs)
with self:
self.input = Node(label="In", size_in=2)
self.ensemble = Ensemble(n_neurons, dimensions=2, label="Oscillator")
tA = [[1, -frequency * recurrent_tau], [frequency * recurrent_tau, 1]]
Connection(
self.ensemble, self.ensemble, synapse=recurrent_tau, transform=tA
)
Connection(self.input, self.ensemble, synapse=None)
self.output = self.ensemble
def reroute(conn):
c = Connection(
self.thresh_ens.output,
conn.post,
transform=conn.transform,
synapse=conn.synapse,
)
self.connections.remove(conn)
return c
def __init__(
self,
n_neurons,
n_ensembles,
ens_dimensions=1,
label=None,
seed=None,
add_to_container=None,
**ens_kwargs,
):
if "dimensions" in ens_kwargs:
raise ValidationError(
"'dimensions' is not a valid argument to EnsembleArray. "
"To set the number of ensembles, use 'n_ensembles'. To set "
"the number of dimensions per ensemble, use 'ens_dimensions'.",
attr="dimensions",
obj=self,
)
super().__init__(label, seed, add_to_container)
for param, value in ens_kwargs.items():
if is_iterable(value):
ens_kwargs[param] = Samples(value)
self.config[Ensemble].update(ens_kwargs)
label_prefix = "" if label is None else label + "_"
self.n_neurons_per_ensemble = n_neurons
self.n_ensembles = n_ensembles
self.dimensions_per_ensemble = ens_dimensions
# These may be set in add_neuron_input and add_neuron_output
self.neuron_input, self.neuron_output = None, None
self.ea_ensembles = []
with self:
self.input = Node(size_in=self.dimensions, label="input")
for i in range(n_ensembles):
e = Ensemble(
n_neurons,
self.dimensions_per_ensemble,
label=f"{label_prefix}{i}",
)
Connection(
self.input[i * ens_dimensions : (i + 1) * ens_dimensions],
e,
synapse=None,
)
self.ea_ensembles.append(e)
self.add_output("output", function=None)
def add_output_mapping(self, name, output_vectors):
"""Adds another output to the associative memory network.
Creates a transform with the given output vectors between the
associative memory element output and a named output node to enable the
selection of output vectors by the associative memory.
Parameters
----------
name: str
Name to use for the output node. This name will be used as
the name of the attribute for the associative memory network.
output_vectors: array_like
The list of vectors to be produced for each match.
"""
# --- Put arguments in canonical form
if is_iterable(output_vectors):
output_vectors = np.array(output_vectors, ndmin=2)
# --- Check preconditions
if hasattr(self, name):
raise ValidationError(
"Name '%s' already exists as a node in the "
"associative memory." % name,
attr="name",
)
# --- Make the output node and connect it
output = Node(size_in=output_vectors.shape[1], label=name)
setattr(self, name, output)
if self.thresh_ens is not None:
c = Connection(self.thresh_ens.output,
output,
synapse=None,
transform=output_vectors.T)
else:
c = Connection(self.elem_output,
output,
synapse=None,
transform=output_vectors.T)
self.out_conns.append(c)
def conn_probe(model, probe):
conn = Connection(probe.target, probe, synapse=probe.synapse,
solver=probe.solver, add_to_container=False)
# Set connection's seed to probe's (which isn't used elsewhere)
model.seeds[conn] = model.seeds[probe]
# Make a sink signal for the connection
model.sig[probe]['in'] = Signal(np.zeros(conn.size_out), name=str(probe))
model.add_op(Reset(model.sig[probe]['in']))
# Build the connection
model.build(conn)
def add_input_mapping(self, name, input_vectors, input_scales=1.0):
"""Adds a set of input vectors to the associative memory network.
Creates a transform with the given input vectors between the
a named input node and associative memory element input to enable the
inputs to be mapped onto ensembles of the Associative Memory.
Parameters
----------
name: str
Name to use for the input node. This name will be used as the name
of the attribute for the associative memory network.
input_vectors: array_like
The list of vectors to be compared against.
input_scales: float or array_like, optional
Scaling factor to apply on each of the input vectors. Note that it
is possible to scale each vector independently.
"""
# --- Put arguments in canonical form
n_vectors, d_vectors = input_vectors.shape
if is_iterable(input_vectors):
input_vectors = np.array(input_vectors, ndmin=2)
if not is_iterable(input_scales):
input_scales = input_scales * np.ones((1, n_vectors))
else:
input_scales = np.array(input_scales, ndmin=2)
# --- Check some preconditions
if input_scales.shape[1] != n_vectors:
raise ValidationError(
"Number of input_scale values (%d) does not "
"match number of input vectors (%d)." %
(input_scales.shape[1], n_vectors),
attr="input_scales",
)
if hasattr(self, name):
raise ValidationError(
"Name '%s' already exists as a node in the "
"associative memory." % name,
attr="name",
)
# --- Finally, make the input node and connect it
in_node = Node(size_in=d_vectors, label=name)
setattr(self, name, in_node)
Connection(
in_node,
self.elem_input,
synapse=None,
transform=input_vectors * input_scales.T,
)
def __init__(
self,
n_neurons,
dimensions,
invert_a=False,
invert_b=False,
input_magnitude=1.0,
**kwargs,
):
if "net" in kwargs:
raise ObsoleteError("The 'net' argument is no longer supported.")
kwargs.setdefault("label", "Circular convolution")
super().__init__(**kwargs)
tr_a = transform_in(dimensions, "A", invert_a)
tr_b = transform_in(dimensions, "B", invert_b)
tr_out = transform_out(dimensions)
with self:
self.input_a = Node(size_in=dimensions, label="input_a")
self.input_b = Node(size_in=dimensions, label="input_b")
self.product = Product(n_neurons,
tr_out.shape[1],
input_magnitude=input_magnitude * 2)
self.output = Node(size_in=dimensions, label="output")
Connection(self.input_a,
self.product.input_a,
transform=tr_a,
synapse=None)
Connection(self.input_b,
self.product.input_b,
transform=tr_b,
synapse=None)
Connection(self.product.output,
self.output,
transform=tr_out,
synapse=None)
def conn_probe(model, probe):
# Connection probes create a connection from the target, and probe
# the resulting signal (used when you want to probe the default
# output of an object, which may not have a predefined signal)
conn = Connection(probe.target, probe, synapse=probe.synapse,
solver=probe.solver, add_to_container=False)
# Set connection's seed to probe's (which isn't used elsewhere)
model.seeds[conn] = model.seeds[probe]
# Make a sink signal for the connection
model.sig[probe]['in'] = Signal(np.zeros(conn.size_out), name=str(probe))
model.add_op(Reset(model.sig[probe]['in']))
# Build the connection
model.build(conn)
def _create_replacement_connection(c_in, c_out):
"""Generate a new Connection to replace two through a passthrough Node."""
# imported here to avoid circular imports
# pylint: disable=import-outside-toplevel
from nengo.connection import Connection
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 Unconvertible("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 Unconvertible("Cannot remove a connection with a function")
# 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 = Connection(
c_in.pre_obj,
c_out.post_obj,
synapse=synapse,
transform=transform,
function=function,
add_to_container=False,
)
return c
def add_wta_network(self, inhibit_scale=1.5, inhibit_synapse=0.005):
"""Add a winner-take-all (WTA) network to associative memory output.
Parameters
----------
inhibit_scale: float, optional
Mutual inhibition scaling factor.
inhibit_synapse: float, optional
Mutual inhibition synapse time constant.
"""
if not self.is_wta:
Connection(
self.elem_output,
self.elem_input,
synapse=inhibit_synapse,
transform=((np.eye(self.n_items) - 1) * inhibit_scale),
)
self.is_wta = True
else:
warnings.warn("AssociativeMemory network is already configured "
"with a WTA network. Additional `add_wta_network` "
"calls are ignored.")
def __init__(self, n_neurons, dimensions, input_magnitude=1.0, **kwargs):
if "net" in kwargs:
raise ObsoleteError("The 'net' argument is no longer supported.")
kwargs.setdefault("label", "Product")
super().__init__(**kwargs)
with self:
self.input_a = Node(size_in=dimensions, label="input_a")
self.input_b = Node(size_in=dimensions, label="input_b")
self.output = Node(size_in=dimensions, label="output")
self.sq1 = EnsembleArray(
max(1, n_neurons // 2),
n_ensembles=dimensions,
ens_dimensions=1,
radius=input_magnitude * np.sqrt(2),
)
self.sq2 = EnsembleArray(
max(1, n_neurons // 2),
n_ensembles=dimensions,
ens_dimensions=1,
radius=input_magnitude * np.sqrt(2),
)
tr = 1.0 / np.sqrt(2.0)
Connection(self.input_a,
self.sq1.input,
transform=tr,
synapse=None)
Connection(self.input_b,
self.sq1.input,
transform=tr,
synapse=None)
Connection(self.input_a,
self.sq2.input,
transform=tr,
synapse=None)
Connection(self.input_b,
self.sq2.input,
transform=-tr,
synapse=None)
sq1_out = self.sq1.add_output("square", np.square)
Connection(sq1_out, self.output, transform=0.5, synapse=None)
sq2_out = self.sq2.add_output("square", np.square)
Connection(sq2_out, self.output, transform=-0.5, synapse=None)
def add_output(self, name, function, synapse=None, **conn_kwargs):
"""Adds a node that collects the decoded output of all ensembles.
By default, this is called once in ``__init__`` with ``function=None``.
However, this can be called multiple times with different functions,
similar to the way in which an ensemble can be connected to many
downstream ensembles with different functions.
Note that in addition to the parameters below, parameters affecting
all of the connections from the sub-ensembles to the new node
can be passed to this function. For example:
.. testcode::
ea.add_output("lstsq_output", None, solver=nengo.solvers.Lstsq())
creates a new output at ``ea.lstsq_output`` with the decoders
of each connection solved for with the `.Lstsq` solver.
Parameters
----------
name : str
The name of the output. This will also be the name of the attribute
set on the ensemble array.
function : callable or iterable of callables
The function to compute across the connection from sub-ensembles
to the new output node. If function is an iterable, it must be
an iterable consisting of one function for each sub-ensemble.
synapse : Synapse, optional
The synapse model with which to filter the connections from
sub-ensembles to the new output node. This is kept separate from
the other ``conn_kwargs`` because this defaults to None rather
than the default synapse model. In almost all cases the synapse
should stay as None, and synaptic filtering should be performed in
the connection from the output node.
"""
if hasattr(self, name):
raise ValidationError(
f"Cannot add output '{name}'; there is already an attribute "
"with this name",
attr="name",
obj=self,
)
dims_per_ens = self.dimensions_per_ensemble
# get output size for each ensemble
sizes = np.zeros(self.n_ensembles, dtype=int)
if is_iterable(function) and all(callable(f) for f in function):
if len(list(function)) != self.n_ensembles:
raise ValidationError(
"Must have one function per ensemble", attr="function", obj=self
)
for i, func in enumerate(function):
sizes[i] = np.asarray(func(np.zeros(dims_per_ens))).size
elif callable(function):
sizes[:] = np.asarray(function(np.zeros(dims_per_ens))).size
function = [function] * self.n_ensembles
elif function is None:
sizes[:] = dims_per_ens
function = [None] * self.n_ensembles
else:
raise ValidationError(
"'function' must be a callable, list of callables, or None",
attr="function",
obj=self,
)
output = Node(output=None, size_in=sizes.sum(), label=name)
setattr(self, name, output)
indices = np.zeros(len(sizes) + 1, dtype=int)
indices[1:] = np.cumsum(sizes)
for i, e in enumerate(self.ea_ensembles):
Connection(
e,
output[indices[i] : indices[i + 1]],
function=function[i],
synapse=synapse,
**conn_kwargs,
)
return output
def __init__( # noqa: C901
self,
input_vectors,
output_vectors=None,
n_neurons=50,
threshold=0.3,
input_scales=1.0,
inhibitable=False,
label=None,
seed=None,
add_to_container=None,
):
super().__init__(label, seed, add_to_container)
# --- Put arguments in canonical form
if output_vectors is None:
# If output vocabulary is not specified, use input vector list
# (i.e autoassociative memory)
output_vectors = input_vectors
if is_iterable(input_vectors):
input_vectors = np.array(input_vectors, ndmin=2)
if is_iterable(output_vectors):
output_vectors = np.array(output_vectors, ndmin=2)
if input_vectors.shape[0] == 0:
raise ValidationError("Number of input vectors cannot be 0.",
attr="input_vectors",
obj=self)
elif input_vectors.shape[0] != output_vectors.shape[0]:
# Fail if number of input items and number of output items don't
# match
raise ValidationError(
"Number of input vectors does not match number of output "
"vectors. %d != %d" %
(input_vectors.shape[0], output_vectors.shape[0]),
attr="input_vectors",
obj=type(self),
)
# Handle possible different threshold / input_scale values for each
# element in the associative memory
if not is_iterable(threshold):
threshold = threshold * np.ones(input_vectors.shape[0])
else:
threshold = np.array(threshold)
# --- Check preconditions
self.n_items = input_vectors.shape[0]
if threshold.shape[0] != self.n_items:
raise ValidationError(
"Number of threshold values (%d) does not match number of "
"input vectors (%d)." % (threshold.shape[0], self.n_items),
attr="threshold",
obj=self,
)
# --- Set parameters
self.out_conns = [] # Used in `add_threshold_to_output`
# Used in `add_threshold_to_output`
self.default_vector_inhibit_conns = []
self.thresh_ens = None # Will hold thresholded outputs
self.is_wta = False
self._inhib_scale = 1.5
# -- Create the core network
with self, self.am_ens_config:
self.bias_node = Node(output=1)
self.elem_input = Node(size_in=self.n_items, label="element input")
self.elem_output = Node(size_in=self.n_items,
label="element output")
self.utilities = self.elem_output
self.am_ensembles = []
label_prefix = "" if label is None else label + "_"
filt_scale = 15
filt_step_func = lambda x: filtered_step(x, 0.0, scale=filt_scale)
for i in range(self.n_items):
e = Ensemble(n_neurons, 1, label=label_prefix + str(i))
self.am_ensembles.append(e)
# Connect input and output nodes
Connection(self.bias_node, e, transform=-threshold[i])
Connection(self.elem_input[i], e)
Connection(e, self.elem_output[i], function=filt_step_func)
if inhibitable:
# Input node for inhibitory gating signal (if enabled)
self.inhibit = Node(size_in=1, label="inhibit")
Connection(
self.inhibit,
self.elem_input,
transform=-np.ones((self.n_items, 1)) * self._inhib_scale,
)
# Note: We can use a decoded connection here because all the
# am_ensembles have [1] encoders
else:
self.inhibit = None
self.thresh_bias = None
self.thresholded_utilities = None
self.add_input_mapping("input", input_vectors, input_scales)
self.add_output_mapping("output", output_vectors)
def add_threshold_to_outputs(self, n_neurons=50, inhibit_scale=10):
"""Adds a thresholded output to the associative memory.
Parameters
----------
n_neurons: int, optional
Number of neurons to use for the default output vector ensemble.
inhibit_scale: float, optional
Mutual inhibition scaling factor.
"""
if self.thresh_ens is not None:
warnings.warn("AssociativeMemory network is already configured "
"with thresholded outputs. Additional "
"`add_threshold_to_output` calls are ignored.")
return
with self.thresh_ens_config:
self.thresh_bias = EnsembleArray(n_neurons,
self.n_items,
label="thresh_bias")
self.thresh_ens = EnsembleArray(n_neurons,
self.n_items,
label="thresh_ens")
self.thresholded_utilities = self.thresh_ens.output
Connection(
self.bias_node,
self.thresh_bias.input,
transform=np.ones((self.n_items, 1)),
synapse=None,
)
Connection(
self.bias_node,
self.thresh_ens.input,
transform=np.ones((self.n_items, 1)),
synapse=None,
)
Connection(self.elem_output,
self.thresh_bias.input,
transform=-inhibit_scale)
Connection(self.thresh_bias.output,
self.thresh_ens.input,
transform=-inhibit_scale)
# Reroute connections from elem_output to be connections
# from thresh_ens.output
def reroute(conn):
c = Connection(
self.thresh_ens.output,
conn.post,
transform=conn.transform,
synapse=conn.synapse,
)
self.connections.remove(conn)
return c
self.default_vector_inhibit_conns = [
reroute(c) for c in self.default_vector_inhibit_conns
]
self.out_conns = [reroute(c) for c in self.out_conns]
# Make inhibitory connection if inhibit option is set
if self.inhibit is not None:
for e in self.thresh_ens.ensembles:
Connection(self.inhibit,
e,
transform=-self._inhib_scale,
synapse=None)
def __init__(self,
dimensions,
n_neurons_per_ensemble=100,
output_weight=-3.0,
input_bias=0.0,
ampa_config=None,
gaba_config=None,
**kwargs):
if "net" in kwargs:
raise ObsoleteError("The 'net' argument is no longer supported.")
kwargs.setdefault("label", "Basal Ganglia")
super().__init__(**kwargs)
ampa_config, override_ampa = config_with_default_synapse(
ampa_config, Lowpass(0.002))
gaba_config, override_gaba = config_with_default_synapse(
gaba_config, Lowpass(0.008))
# Affects all ensembles / connections in the BG
# unless they've been overridden on `self.config`
config = Config(Ensemble, Connection)
config[Ensemble].radius = 1.5
config[Ensemble].encoders = Choice([[1]])
try:
# Best, if we have SciPy
config[Connection].solver = NnlsL2nz()
except ImportError:
# Warn if we can't use the better decoder solver.
warnings.warn("SciPy is not installed, so BasalGanglia will "
"use the default decoder solver. Installing SciPy "
"may improve BasalGanglia performance.")
ea_params = {
"n_neurons": n_neurons_per_ensemble,
"n_ensembles": dimensions
}
with self, config:
self.strD1 = EnsembleArray(
label="Striatal D1 neurons",
intercepts=Uniform(Weights.e, 1),
**ea_params,
)
self.strD2 = EnsembleArray(
label="Striatal D2 neurons",
intercepts=Uniform(Weights.e, 1),
**ea_params,
)
self.stn = EnsembleArray(
label="Subthalamic nucleus",
intercepts=Uniform(Weights.ep, 1),
**ea_params,
)
self.gpi = EnsembleArray(
label="Globus pallidus internus",
intercepts=Uniform(Weights.eg, 1),
**ea_params,
)
self.gpe = EnsembleArray(
label="Globus pallidus externus",
intercepts=Uniform(Weights.ee, 1),
**ea_params,
)
self.input = Node(label="input", size_in=dimensions)
self.output = Node(label="output", size_in=dimensions)
# add bias input (BG performs best in the range 0.5--1.5)
if abs(input_bias) > 0.0:
self.bias_input = Node(np.ones(dimensions) * input_bias,
label="basal ganglia bias")
Connection(self.bias_input, self.input)
# spread the input to StrD1, StrD2, and STN
Connection(
self.input,
self.strD1.input,
synapse=None,
transform=Weights.ws * (1 + Weights.lg),
)
Connection(
self.input,
self.strD2.input,
synapse=None,
transform=Weights.ws * (1 - Weights.le),
)
Connection(self.input,
self.stn.input,
synapse=None,
transform=Weights.wt)
# connect the striatum to the GPi and GPe (inhibitory)
strD1_output = self.strD1.add_output("func_str", Weights.str_func)
strD2_output = self.strD2.add_output("func_str", Weights.str_func)
with gaba_config:
Connection(strD1_output, self.gpi.input, transform=-Weights.wm)
Connection(strD2_output, self.gpe.input, transform=-Weights.wm)
# connect the STN to GPi and GPe (broad and excitatory)
tr = Weights.wp * np.ones((dimensions, dimensions))
stn_output = self.stn.add_output("func_stn", Weights.stn_func)
with ampa_config:
Connection(stn_output, self.gpi.input, transform=tr)
Connection(stn_output, self.gpe.input, transform=tr)
# connect the GPe to GPi and STN (inhibitory)
gpe_output = self.gpe.add_output("func_gpe", Weights.gpe_func)
with gaba_config:
Connection(gpe_output, self.gpi.input, transform=-Weights.we)
Connection(gpe_output, self.stn.input, transform=-Weights.wg)
# connect GPi to output (inhibitory)
gpi_output = self.gpi.add_output("func_gpi", Weights.gpi_func)
Connection(gpi_output,
self.output,
synapse=None,
transform=output_weight)
# Return ampa_config and gaba_config to previous states, if changed
if override_ampa:
del ampa_config[Connection].synapse
if override_gaba:
del gaba_config[Connection].synapse