def test_nnls_repr():
pytest.importorskip("scipy.optimize")
check_init_args(Nnls, ["weights"])
check_repr(Nnls(weights=True))
assert repr(Nnls(weights=True)) == "Nnls(weights=True)"
check_init_args(NnlsL2, ["weights", "reg"])
check_repr(NnlsL2(weights=True, reg=0.2))
assert repr(NnlsL2(weights=True, reg=0.2)) == "NnlsL2(weights=True, reg=0.2)"
check_init_args(NnlsL2nz, ["weights", "reg"])
check_repr(NnlsL2nz(weights=True, reg=0.2))
assert repr(NnlsL2nz(weights=True, reg=0.2)) == "NnlsL2nz(weights=True, reg=0.2)"
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, 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 `self.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 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 = nengo.Node(label="input", size_in=dimensions)
self.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:
self.bias_input = nengo.Node(np.ones(dimensions) * input_bias,
label="basal ganglia bias")
nengo.Connection(self.bias_input, self.input)
# spread the input to StrD1, StrD2, and STN
nengo.Connection(
self.input,
self.strD1.input,
synapse=None,
transform=Weights.ws * (1 + Weights.lg),
)
nengo.Connection(
self.input,
self.strD2.input,
synapse=None,
transform=Weights.ws * (1 - Weights.le),
)
nengo.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:
nengo.Connection(strD1_output,
self.gpi.input,
transform=-Weights.wm)
nengo.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:
nengo.Connection(stn_output, self.gpi.input, transform=tr)
nengo.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:
nengo.Connection(gpe_output,
self.gpi.input,
transform=-Weights.we)
nengo.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)
nengo.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[nengo.Connection].synapse
if override_gaba:
del gaba_config[nengo.Connection].synapse
def BasalGanglia(dimensions,
n_neurons_per_ensemble=100,
output_weight=-3.,
input_bias=0.,
ampa_config=None,
gaba_config=None,
net=None):
"""Winner take all network, typically used for action selection.
The basal ganglia network outputs approximately 0 at the dimension with
the largest value, and is negative elsewhere.
While the basal ganglia is primarily defined by its winner-take-all
function, it is also organized to match the organization of the human
basal ganglia. It consists of five ensembles:
* Striatal D1 dopamine-receptor neurons (``strD1``)
* Striatal D2 dopamine-receptor neurons (``strD2``)
* Subthalamic nucleus (``stn``)
* Globus pallidus internus / substantia nigra reticulata (``gpi``)
* Globus pallidus externus (``gpe``)
Interconnections between these areas are also based on known
neuroanatomical connections. See [1]_ for more details, and [2]_ for
the original non-spiking basal ganglia model by
Gurney, Prescott & Redgrave that this model is based on.
.. note:: The default `.Solver` for the basal ganglia is `.NnlsL2nz`, which
requires SciPy. If SciPy is not installed, the global default
solver will be used instead.
Parameters
----------
dimensions : int
Number of dimensions (i.e., actions).
n_neurons_per_ensemble : int, optional (Default: 100)
Number of neurons in each ensemble in the network.
output_weight : float, optional (Default: -3.)
A scaling factor on the output of the basal ganglia
(specifically on the connection out of the GPi).
input_bias : float, optional (Default: 0.)
An amount by which to bias all dimensions of the input node.
Biasing the input node is important for ensuring that all input
dimensions are positive and easily comparable.
ampa_config : config, optional (Default: None)
Configuration for connections corresponding to biological connections
to AMPA receptors (i.e., connections from STN to to GPi and GPe).
If None, a default configuration using a 2 ms lowpass synapse
will be used.
gaba_config : config, optional (Default: None)
Configuration for connections corresponding to biological connections
to GABA receptors (i.e., connections from StrD1 to GPi, StrD2 to GPe,
and GPe to GPi and STN). If None, a default configuration using an
8 ms lowpass synapse will be used.
net : Network, optional (Default: None)
A network in which the network components will be built.
This is typically used to provide a custom set of Nengo object
defaults through modifying ``net.config``.
Returns
-------
net : Network
The newly built basal ganglia network, or the provided ``net``.
Attributes
----------
net.bias_input : Node or None
If ``input_bias`` is non-zero, this node will be created to bias
all of the dimensions of the input signal.
net.gpe : EnsembleArray
Globus pallidus externus ensembles.
net.gpi : EnsembleArray
Globus pallidus internus ensembles.
net.input : Node
Accepts the input signal.
net.output : Node
Provides the output signal.
net.stn : EnsembleArray
Subthalamic nucleus ensembles.
net.strD1 : EnsembleArray
Striatal D1 ensembles.
net.strD2 : EnsembleArray
Striatal D2 ensembles.
References
----------
.. [1] Stewart, T. C., Choo, X., & Eliasmith, C. (2010).
Dynamic behaviour of a spiking model of action selection in the
basal ganglia. In Proceedings of the 10th international conference on
cognitive modeling (pp. 235-40).
.. [2] Gurney, K., Prescott, T., & Redgrave, P. (2001).
A computational model of action selection in the basal
ganglia. Biological Cybernetics 84, 401-423.
"""
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 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
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 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
def __init__(self,
dimensions,
n_neurons_per_ensemble=100,
radius=1.5,
tau_ampa=0.002,
tau_gaba=0.008,
output_weight=-3,
input_bias=0.0,
solver=None):
if solver is None:
try:
# Best, if we have SciPy
solver = NnlsL2nz()
except ImportError:
# If not, use default
warnings.warn("SciPy is not installed, so BasalGanglia will "
"use default decoder solver. Installing SciPy "
"may improve BasalGanglia performance.")
solver = nengo.Default
encoders = np.ones((n_neurons_per_ensemble, 1))
ea_params = {
'n_neurons': n_neurons_per_ensemble,
'n_ensembles': dimensions,
'radius': radius,
'encoders': encoders,
}
self.strD1 = EnsembleArray(label="Striatal D1 neurons",
intercepts=Uniform(self.e, 1),
**ea_params)
self.strD2 = EnsembleArray(label="Striatal D2 neurons",
intercepts=Uniform(self.e, 1),
**ea_params)
self.stn = EnsembleArray(label="Subthalamic nucleus",
intercepts=Uniform(self.ep, 1),
**ea_params)
self.gpi = EnsembleArray(label="Globus pallidus internus",
intercepts=Uniform(self.eg, 1),
**ea_params)
self.gpe = EnsembleArray(label="Globus pallidus externus",
intercepts=Uniform(self.ee, 1),
**ea_params)
self.input = nengo.Node(label="input", size_in=dimensions)
self.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:
self.bias_input = nengo.Node([input_bias] * dimensions)
nengo.Connection(self.bias_input, self.input)
# spread the input to StrD1, StrD2, and STN
nengo.Connection(self.input,
self.strD1.input,
synapse=None,
transform=self.ws * (1 + self.lg))
nengo.Connection(self.input,
self.strD2.input,
synapse=None,
transform=self.ws * (1 - self.le))
nengo.Connection(self.input,
self.stn.input,
synapse=None,
transform=self.wt)
# connect the striatum to the GPi and GPe (inhibitory)
strD1_output = self.strD1.add_output('func_str',
self.str_func,
solver=solver)
nengo.Connection(strD1_output,
self.gpi.input,
synapse=tau_gaba,
transform=-np.eye(dimensions) * self.wm)
strD2_output = self.strD2.add_output('func_str',
self.str_func,
solver=solver)
nengo.Connection(strD2_output,
self.gpe.input,
synapse=tau_gaba,
transform=-np.eye(dimensions) * self.wm)
# connect the STN to GPi and GPe (broad and excitatory)
tr = self.wp * np.ones((dimensions, dimensions))
stn_output = self.stn.add_output('func_stn',
self.stn_func,
solver=solver)
nengo.Connection(stn_output,
self.gpi.input,
transform=tr,
synapse=tau_ampa)
nengo.Connection(stn_output,
self.gpe.input,
transform=tr,
synapse=tau_ampa)
# connect the GPe to GPi and STN (inhibitory)
gpe_output = self.gpe.add_output('func_gpe',
self.gpe_func,
solver=solver)
nengo.Connection(gpe_output,
self.gpi.input,
synapse=tau_gaba,
transform=-self.we)
nengo.Connection(gpe_output,
self.stn.input,
synapse=tau_gaba,
transform=-self.wg)
# connect GPi to output (inhibitory)
gpi_output = self.gpi.add_output('func_gpi',
self.gpi_func,
solver=solver)
nengo.Connection(gpi_output,
self.output,
synapse=None,
transform=output_weight)