def add_default_output_vector(self, output_vector, output_name='output',
n_neurons=50, min_activation_value=0.5):
# Default configuration to use for the ensembles
default_ens_config = nengo.Config(nengo.Ensemble)
default_ens_config[nengo.Ensemble].radius = 1
default_ens_config[nengo.Ensemble].intercepts = \
ClippedExpDist(self.exp_scale, 0.0, 1.0)
default_ens_config[nengo.Ensemble].encoders = Choice([[1]])
default_ens_config[nengo.Ensemble].eval_points = Uniform(0.0, 1.0)
default_ens_config[nengo.Ensemble].n_eval_points = self.n_eval_points
with nested(self, default_ens_config):
default_vector_ens = nengo.Ensemble(n_neurons, 1,
label=('Default %s vector' %
output_name))
nengo.Connection(self.bias_node, default_vector_ens,
synapse=None)
if self.thresh_ens is not None:
c = nengo.Connection(self.thresh_ens.output,
default_vector_ens,
transform=(-(1.0 / min_activation_value) *
np.ones((1, self.num_items))))
else:
c = nengo.Connection(self.elem_output, default_vector_ens,
transform=(-(1.0 / min_activation_value) *
np.ones((1, self.num_items))))
self.default_output_utility = default_vector_ens
self.default_output_thresholded_utility = default_vector_ens
# 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)
nengo.Connection(default_vector_ens, output,
transform=np.matrix(output_vector).T,
synapse=None)
if self.inhibit is not None:
nengo.Connection(self.inhibit, default_vector_ens,
transform=-1.0, synapse=None)
def Product(n_neurons, dimensions, input_magnitude=1, config=None, net=None):
"""Computes the element-wise product of two equally sized vectors.
The network used to calculate the product is described in
`Precise multiplications with the NEF
<http://nbviewer.ipython.org/github/ctn-archive/technical-reports/blob/master/Precise-multiplications-with-the-NEF.ipynb#An-alternative-network>`_.
A simpler version of this network can be found in the `Multiplication
example <http://pythonhosted.org/nengo/examples/multiplication.html>`_.
"""
if net is None:
net = nengo.Network(label="Product")
if config is None:
config = nengo.Config(nengo.Ensemble)
with nested(net, config):
net.A = nengo.Node(size_in=dimensions, label="A")
net.B = nengo.Node(size_in=dimensions, label="B")
net.output = nengo.Node(size_in=dimensions, label="output")
net.sq1 = EnsembleArray(max(1, n_neurons // 2),
n_ensembles=dimensions,
ens_dimensions=1,
radius=input_magnitude * np.sqrt(2))
net.sq2 = EnsembleArray(max(1, n_neurons // 2),
n_ensembles=dimensions,
ens_dimensions=1,
radius=input_magnitude * np.sqrt(2))
tr = 1. / np.sqrt(2.)
nengo.Connection(net.A, net.sq1.input, transform=tr, synapse=None)
nengo.Connection(net.B, net.sq1.input, transform=tr, synapse=None)
nengo.Connection(net.A, net.sq2.input, transform=tr, synapse=None)
nengo.Connection(net.B, net.sq2.input, transform=-tr, synapse=None)
sq1_out = net.sq1.add_output('square', np.square)
nengo.Connection(sq1_out, net.output, transform=.5)
sq2_out = net.sq2.add_output('square', np.square)
nengo.Connection(sq2_out, net.output, transform=-.5)
return net
def Product(n_neurons, dimensions, input_magnitude=1, config=None, net=None):
"""Computes the element-wise product of two equally sized vectors."""
if net is None:
net = nengo.Network(label="Product")
if config is None:
config = nengo.Config(nengo.Ensemble)
config[nengo.Ensemble].encoders = Choice(
[[1, 1], [1, -1], [-1, 1], [-1, -1]])
with nested(net, config):
net.A = nengo.Node(size_in=dimensions, label="A")
net.B = nengo.Node(size_in=dimensions, label="B")
net.output = nengo.Node(size_in=dimensions, label="output")
net.product = EnsembleArray(n_neurons, n_ensembles=dimensions,
ens_dimensions=2,
radius=input_magnitude * np.sqrt(2))
nengo.Connection(net.A, net.product.input[::2], synapse=None)
nengo.Connection(net.B, net.product.input[1::2], synapse=None)
net.output = net.product.add_output('product', lambda x: x[0] * x[1])
return net
def Product(n_neurons, dimensions, input_magnitude=1, config=None, net=None):
"""Computes the element-wise product of two equally sized vectors."""
if net is None:
net = nengo.Network(label="Product")
if config is None:
config = nengo.Config(nengo.Ensemble)
config[nengo.Ensemble].encoders = Choice(
[[1, 1], [1, -1], [-1, 1], [-1, -1]])
with nested(net, config):
net.A = nengo.Node(size_in=dimensions, label="A")
net.B = nengo.Node(size_in=dimensions, label="B")
net.output = nengo.Node(size_in=dimensions, label="output")
net.product = EnsembleArray(n_neurons, n_ensembles=dimensions,
ens_dimensions=2,
radius=input_magnitude * np.sqrt(2))
nengo.Connection(net.A, net.product.input[::2], synapse=None)
nengo.Connection(net.B, net.product.input[1::2], synapse=None)
net.output = net.product.add_output('product', lambda x: x[0] * x[1])
return net
def __init__(self, input_vectors, output_vectors=None, # noqa: C901
n_neurons=50, threshold=0.3, input_scales=1.0,
inhibitable=False, inhibit_scale=1.5, label=None, seed=None,
add_to_container=None):
super(AssociativeMemory, self).__init__(label, seed, add_to_container)
# Label prefix for all the ensemble labels
label_prefix = "" if label is None else label + "_"
# If output vocabulary is not specified, use input vector list
# (i.e autoassociative memory)
if output_vectors is None:
output_vectors = input_vectors
# Handle different vector list types
if is_iterable(input_vectors):
input_vectors = np.matrix(input_vectors)
if is_iterable(output_vectors):
output_vectors = np.matrix(output_vectors)
# Fail if number of input items and number of output items don't
# match
if input_vectors.shape[0] != output_vectors.shape[0]:
raise ValueError(
'Number of input vectors does not match number of output '
'vectors. %d != %d'
% (input_vectors.shape[0], output_vectors.shape[0]))
# Handle possible different threshold / input_scale values for each
# element in the associative memory
if not is_iterable(threshold):
threshold = np.array([threshold] * input_vectors.shape[0])
else:
threshold = np.array(threshold)
if threshold.shape[0] != input_vectors.shape[0]:
raise ValueError(
'Number of threshold values do not match number of input'
'vectors. Got: %d, expected %d.' %
(threshold.shape[0], input_vectors.shape[0]))
# Handle scaling of each input vector
if not is_iterable(input_scales):
input_scale = np.matrix([input_scales] * input_vectors.shape[0])
else:
input_scale = np.matrix(input_scale)
if input_scale.shape[1] != input_vectors.shape[0]:
raise ValueError(
'Number of input_scale values do not match number of input'
'vectors. Got: %d, expected %d.' %
(input_scale.shape[1], input_vectors.shape[0]))
# Input and output nodes
N = input_vectors.shape[0]
self.num_items = N
# Scaling factor for exponential distribution and filtered step
# function
self.exp_scale = 0.15
filt_scale = 15
self.filt_step_func = \
lambda x: filtered_step(x, 0.0, scale=filt_scale)
# Evaluation points parameters
self.n_eval_points = 5000
# Default configuration to use for the ensembles
am_ens_config = nengo.Config(nengo.Ensemble)
am_ens_config[nengo.Ensemble].radius = 1
am_ens_config[nengo.Ensemble].intercepts = \
ClippedExpDist(self.exp_scale, 0.0, 1.0)
am_ens_config[nengo.Ensemble].encoders = Choice([[1]])
am_ens_config[nengo.Ensemble].eval_points = Uniform(0.0, 1.0)
am_ens_config[nengo.Ensemble].n_eval_points = self.n_eval_points
# Store output connections (need to redo them if output thresholding
# is added by the user - see add_threshold_to_output)
self.out_conns = []
# Store the inhibitory connections from elem_output to the default
# vector ensembles (need to redo them if output thresholding
# is added by the user - see add_threshold_to_output)
self.default_vector_inhibit_conns = []
# Flag to indicate if the am network is using thresholded outputs
self.thresh_ens = None
# Flag to indicate if the am network is configured with wta
self._using_wta = False
# Create the associative memory network
with nested(self, am_ens_config):
self.bias_node = nengo.Node(output=1)
self.input = nengo.Node(size_in=input_vectors.shape[1],
label="input")
self.output = nengo.Node(size_in=output_vectors.shape[1],
label="output")
self.elem_input = nengo.Node(size_in=N, label="element input")
self.elem_output = nengo.Node(size_in=N, label="element output")
nengo.Connection(self.input, self.elem_input, synapse=None,
transform=np.multiply(input_vectors,
input_scale.T))
# Make each ensemble
self.am_ensembles = []
for i in range(N):
# Create ensemble
e = nengo.Ensemble(n_neurons, 1, label=label_prefix + str(i))
self.am_ensembles.append(e)
# Connect input and output nodes
nengo.Connection(self.bias_node, e, transform=-threshold[i],
synapse=None)
nengo.Connection(self.elem_input[i], e, synapse=None)
nengo.Connection(e, self.elem_output[i],
function=self.filt_step_func, synapse=None)
# Configure associative memory to be inhibitable
if inhibitable:
# Input node for inhibitory gating signal (if enabled)
self.inhibit = nengo.Node(size_in=1, label="inhibit")
nengo.Connection(self.inhibit, self.elem_input,
transform=-np.ones((N, 1)) * inhibit_scale,
synapse=None)
# Note: We can use decoded connection here because all the
# encoding vectors are [1]
else:
self.inhibit = None
# Configure utilities output
self.utilities = self.elem_output
c = nengo.Connection(self.elem_output, self.output,
transform=output_vectors.T, synapse=None)
# Add the output connection to the output connection list
self.out_conns.append(c)
def add_threshold_to_outputs(self, n_neurons=50, inhibit_scale=10):
if self.thresh_ens is not None:
warnings.warn('AssociativeMemory network is already configured ' +
'with thresholded outputs. Additional ' +
'add_threshold_to_output function calls are ' +
'ignored.')
return
# Default configuration to use for the ensembles
thresh_ens_config = nengo.Config(nengo.Ensemble)
thresh_ens_config[nengo.Ensemble].radius = 1
thresh_ens_config[nengo.Ensemble].intercepts = Uniform(0.5, 1.0)
thresh_ens_config[nengo.Ensemble].encoders = Choice([[1]])
thresh_ens_config[nengo.Ensemble].eval_points = Uniform(0.75, 1.1)
thresh_ens_config[nengo.Ensemble].n_eval_points = self.n_eval_points
with nested(self, thresh_ens_config):
self.thresh_bias = EnsembleArray(n_neurons, self.num_items,
label='thresh_bias')
self.thresh_ens = EnsembleArray(n_neurons, self.num_items,
label='thresh_ens')
nengo.Connection(self.bias_node, self.thresh_bias.input,
transform=np.ones((self.num_items, 1)),
synapse=None)
nengo.Connection(self.bias_node, self.thresh_ens.input,
transform=np.ones((self.num_items, 1)),
synapse=None)
nengo.Connection(self.elem_output, self.thresh_bias.input,
transform=-inhibit_scale)
nengo.Connection(self.thresh_bias.output, self.thresh_ens.input,
transform=-inhibit_scale)
self.thresholded_utilities = self.thresh_ens.output
# Reroute the thresh_ens output to default vector ensembles,
# and remove the original connections
conn_list = []
for conn in self.default_vector_inhibit_conns:
c = nengo.Connection(self.thresh_ens.output, conn.post,
transform=conn.transform,
synapse=conn.synapse)
self.connections.remove(conn)
conn_list.append(c)
self.default_vector_inhibit_conns = conn_list
# Reroute the thresh_ens output to the output nodes, and remove the
# original connections
conn_list = []
for conn in self.out_conns:
c = nengo.Connection(self.thresh_ens.output, conn.post,
transform=conn.transform,
synapse=conn.synapse)
self.connections.remove(conn)
conn_list.append(c)
self.out_conns = conn_list
# Make inhibitory connection if inhibit option is set
if self.inhibit is not None:
for e in self.thresh_ens.ensembles:
nengo.Connection(self.inhibit, e,
transform=-1.5, synapse=None)
def __init__(self, input_vectors, output_vectors=None, # noqa: C901
n_neurons=50, threshold=0.3, input_scales=1.0,
inhibitable=False,
label=None, seed=None, add_to_container=None):
super(AssociativeMemory, self).__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 ValueError('Number of input vectors cannot be 0.')
elif input_vectors.shape[0] != output_vectors.shape[0]:
# Fail if number of input items and number of output items don't
# match
raise ValueError(
'Number of input vectors does not match number of output '
'vectors. %d != %d'
% (input_vectors.shape[0], output_vectors.shape[0]))
# 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 self.n_items != output_vectors.shape[0]:
raise ValueError(
"Number of input vectors (%d) does not match number of output "
"vectors (%d)" % (self.n_items, output_vectors.shape[0]))
if threshold.shape[0] != self.n_items:
raise ValueError(
"Number of threshold values (%d) does not match number of "
"input vectors (%d)." % (threshold.shape[0], self.n_items))
# --- 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 nested(self, self.am_ens_config):
self.bias_node = nengo.Node(output=1)
self.elem_input = nengo.Node(
size_in=self.n_items, label="element input")
self.elem_output = nengo.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 = nengo.Ensemble(n_neurons, 1, label=label_prefix + str(i))
self.am_ensembles.append(e)
# Connect input and output nodes
nengo.Connection(self.bias_node, e, transform=-threshold[i])
nengo.Connection(self.elem_input[i], e)
nengo.Connection(
e, self.elem_output[i], function=filt_step_func)
if inhibitable:
# Input node for inhibitory gating signal (if enabled)
self.inhibit = nengo.Node(size_in=1, label="inhibit")
nengo.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.add_input_mapping("input", input_vectors, input_scales)
self.add_output_mapping("output", output_vectors)
def BasalGanglia(dimensions, n_neurons_per_ensemble=100, output_weight=-3,
input_bias=0.0, ampa_config=None, gaba_config=None, net=None):
"""Winner takes all; outputs 0 at max dimension, negative elsewhere."""
if net is None:
net = nengo.Network("Basal Ganglia")
ampa_config, override_ampa = config_with_default_synapse(
ampa_config, nengo.Lowpass(0.002))
gaba_config, override_gaba = config_with_default_synapse(
gaba_config, nengo.Lowpass(0.008))
# Affects all ensembles / connections in the BG
# unless they've been overridden on `net.config`
config = nengo.Config(nengo.Ensemble, nengo.Connection)
config[nengo.Ensemble].radius = 1.5
config[nengo.Ensemble].encoders = Choice([[1]])
try:
# Best, if we have SciPy
config[nengo.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 nested(config, net):
net.strD1 = EnsembleArray(label="Striatal D1 neurons",
intercepts=Uniform(Weights.e, 1),
**ea_params)
net.strD2 = EnsembleArray(label="Striatal D2 neurons",
intercepts=Uniform(Weights.e, 1),
**ea_params)
net.stn = EnsembleArray(label="Subthalamic nucleus",
intercepts=Uniform(Weights.ep, 1),
**ea_params)
net.gpi = EnsembleArray(label="Globus pallidus internus",
intercepts=Uniform(Weights.eg, 1),
**ea_params)
net.gpe = EnsembleArray(label="Globus pallidus externus",
intercepts=Uniform(Weights.ee, 1),
**ea_params)
net.input = nengo.Node(label="input", size_in=dimensions)
net.output = nengo.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:
net.bias_input = nengo.Node(np.ones(dimensions) * input_bias)
nengo.Connection(net.bias_input, net.input)
# spread the input to StrD1, StrD2, and STN
nengo.Connection(net.input, net.strD1.input, synapse=None,
transform=Weights.ws * (1 + Weights.lg))
nengo.Connection(net.input, net.strD2.input, synapse=None,
transform=Weights.ws * (1 - Weights.le))
nengo.Connection(net.input, net.stn.input, synapse=None,
transform=Weights.wt)
# connect the striatum to the GPi and GPe (inhibitory)
strD1_output = net.strD1.add_output('func_str', Weights.str_func)
strD2_output = net.strD2.add_output('func_str', Weights.str_func)
with gaba_config:
nengo.Connection(strD1_output, net.gpi.input,
transform=-Weights.wm)
nengo.Connection(strD2_output, net.gpe.input,
transform=-Weights.wm)
# connect the STN to GPi and GPe (broad and excitatory)
tr = Weights.wp * np.ones((dimensions, dimensions))
stn_output = net.stn.add_output('func_stn', Weights.stn_func)
with ampa_config:
nengo.Connection(stn_output, net.gpi.input, transform=tr)
nengo.Connection(stn_output, net.gpe.input, transform=tr)
# connect the GPe to GPi and STN (inhibitory)
gpe_output = net.gpe.add_output('func_gpe', Weights.gpe_func)
with gaba_config:
nengo.Connection(gpe_output, net.gpi.input, transform=-Weights.we)
nengo.Connection(gpe_output, net.stn.input, transform=-Weights.wg)
# connect GPi to output (inhibitory)
gpi_output = net.gpi.add_output('func_gpi', Weights.gpi_func)
nengo.Connection(gpi_output, net.output, synapse=None,
transform=output_weight)
# Return ampa_config and gaba_config to previous states, if changed
if override_ampa:
del ampa_config[nengo.Connection].synapse
if override_gaba:
del gaba_config[nengo.Connection].synapse
return net
def BasalGanglia(dimensions,
n_neurons_per_ensemble=100,
output_weight=-3,
input_bias=0.0,
ampa_config=None,
gaba_config=None,
net=None):
"""Winner takes all; outputs 0 at max dimension, negative elsewhere."""
if net is None:
net = nengo.Network("Basal Ganglia")
ampa_config, override_ampa = config_with_default_synapse(
ampa_config, nengo.Lowpass(0.002))
gaba_config, override_gaba = config_with_default_synapse(
gaba_config, nengo.Lowpass(0.008))
# Affects all ensembles / connections in the BG
# unless they've been overridden on `net.config`
config = nengo.Config(nengo.Ensemble, nengo.Connection)
config[nengo.Ensemble].radius = 1.5
config[nengo.Ensemble].encoders = Choice([[1]])
try:
# Best, if we have SciPy
config[nengo.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 nested(config, net):
net.strD1 = EnsembleArray(label="Striatal D1 neurons",
intercepts=Uniform(Weights.e, 1),
**ea_params)
net.strD2 = EnsembleArray(label="Striatal D2 neurons",
intercepts=Uniform(Weights.e, 1),
**ea_params)
net.stn = EnsembleArray(label="Subthalamic nucleus",
intercepts=Uniform(Weights.ep, 1),
**ea_params)
net.gpi = EnsembleArray(label="Globus pallidus internus",
intercepts=Uniform(Weights.eg, 1),
**ea_params)
net.gpe = EnsembleArray(label="Globus pallidus externus",
intercepts=Uniform(Weights.ee, 1),
**ea_params)
net.input = nengo.Node(label="input", size_in=dimensions)
net.output = nengo.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:
net.bias_input = nengo.Node(np.ones(dimensions) * input_bias,
label="basal ganglia bias")
nengo.Connection(net.bias_input, net.input)
# spread the input to StrD1, StrD2, and STN
nengo.Connection(net.input,
net.strD1.input,
synapse=None,
transform=Weights.ws * (1 + Weights.lg))
nengo.Connection(net.input,
net.strD2.input,
synapse=None,
transform=Weights.ws * (1 - Weights.le))
nengo.Connection(net.input,
net.stn.input,
synapse=None,
transform=Weights.wt)
# connect the striatum to the GPi and GPe (inhibitory)
strD1_output = net.strD1.add_output('func_str', Weights.str_func)
strD2_output = net.strD2.add_output('func_str', Weights.str_func)
with gaba_config:
nengo.Connection(strD1_output,
net.gpi.input,
transform=-Weights.wm)
nengo.Connection(strD2_output,
net.gpe.input,
transform=-Weights.wm)
# connect the STN to GPi and GPe (broad and excitatory)
tr = Weights.wp * np.ones((dimensions, dimensions))
stn_output = net.stn.add_output('func_stn', Weights.stn_func)
with ampa_config:
nengo.Connection(stn_output, net.gpi.input, transform=tr)
nengo.Connection(stn_output, net.gpe.input, transform=tr)
# connect the GPe to GPi and STN (inhibitory)
gpe_output = net.gpe.add_output('func_gpe', Weights.gpe_func)
with gaba_config:
nengo.Connection(gpe_output, net.gpi.input, transform=-Weights.we)
nengo.Connection(gpe_output, net.stn.input, transform=-Weights.wg)
# connect GPi to output (inhibitory)
gpi_output = net.gpi.add_output('func_gpi', Weights.gpi_func)
nengo.Connection(gpi_output,
net.output,
synapse=None,
transform=output_weight)
# Return ampa_config and gaba_config to previous states, if changed
if override_ampa:
del ampa_config[nengo.Connection].synapse
if override_gaba:
del gaba_config[nengo.Connection].synapse
return net
