Skip to content

Commit

Permalink
Merge pull request #223 from Huizerd/master
Browse files Browse the repository at this point in the history 
Correcting the use of time constants
  • Loading branch information
@djsaunde
djsaunde committed Apr 3, 2019
2 parents 727ed38 + e9ba7e4 commit 0e2bb60
Show file tree
Hide file tree
Showing 3 changed files with 102 additions and 106 deletions.
70 changes: 32 additions & 38 deletions bindsnet/models/__init__.py
View file
Original file line number Diff line number Diff line change
@@ -1,14 +1,14 @@
import torch
import numpy as np
from typing import Optional, Union, Tuple, List, Sequence

from torch.nn.modules.utils import _pair
import numpy as np
import torch
from scipy.spatial.distance import euclidean
from typing import Optional, Union, Tuple, List, Sequence
from torch.nn.modules.utils import _pair

from ..network import Network
from ..learning import PostPre
from ..network.topology import Connection, LocallyConnectedConnection
from ..network import Network
from ..network.nodes import Input, RealInput, LIFNodes, DiehlAndCookNodes
from ..network.topology import Connection, LocallyConnectedConnection


class TwoLayerNetwork(Network):
Expand Down Expand Up @@ -37,9 +37,9 @@ def __init__(self, n_inpt: int, n_neurons: int = 100, dt: float = 1.0, wmin: flo
self.n_neurons = n_neurons
self.dt = dt

self.add_layer(Input(n=self.n_inpt, traces=True, trace_tc=5e-2), name='X')
self.add_layer(Input(n=self.n_inpt, traces=True, tc_trace=20.0), name='X')
self.add_layer(LIFNodes(n=self.n_neurons, traces=True, rest=-65.0, reset=-65.0, thresh=-52.0, refrac=5,
decay=1e-2, trace_tc=5e-2), name='Y')
tc_decay=100.0, tc_trace=20.0), name='Y')

w = 0.3 * torch.rand(self.n_inpt, self.n_neurons)
self.add_connection(Connection(source=self.layers['X'], target=self.layers['Y'], w=w, update_rule=PostPre,
Expand All @@ -56,9 +56,7 @@ class DiehlAndCook2015(Network):

def __init__(self, n_inpt: int, n_neurons: int = 100, exc: float = 22.5, inh: float = 17.5, dt: float = 1.0,
nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2), wmin: float = 0.0, wmax: float = 1.0,
norm: float = 78.4, theta_plus: float = 0.05, theta_decay: float = 1e-7,
X_Ae_decay: Optional[float] = None, Ae_Ai_decay: Optional[float] = None,
Ai_Ae_decay: Optional[float] = None) -> None:
norm: float = 78.4, theta_plus: float = 0.05, tc_theta_decay: float = 1e7) -> None:
# language=rst
"""
Constructor for class ``DiehlAndCook2015``.
Expand All @@ -73,10 +71,7 @@ def __init__(self, n_inpt: int, n_neurons: int = 100, exc: float = 22.5, inh: fl
:param wmax: Maximum allowed weight on input to excitatory synapses.
:param norm: Input to excitatory layer connection weights normalization constant.
:param theta_plus: On-spike increment of ``DiehlAndCookNodes`` membrane threshold potential.
:param theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
:param X_Ae_decay: Decay of activation of connection from input to excitatory neurons.
:param Ae_Ai_decay: Decay of activation of connection from excitatory to inhibitory neurons.
:param Ai_Ae_decay: Decay of activation of connection from inhibitory to excitatory neurons.
:param tc_theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
"""
super().__init__(dt=dt)

Expand All @@ -86,28 +81,27 @@ def __init__(self, n_inpt: int, n_neurons: int = 100, exc: float = 22.5, inh: fl
self.inh = inh
self.dt = dt

self.add_layer(Input(n=self.n_inpt, traces=True, trace_tc=5e-2), name='X')
self.add_layer(Input(n=self.n_inpt, traces=True, tc_trace=20.0), name='X')
self.add_layer(DiehlAndCookNodes(n=self.n_neurons, traces=True, rest=-65.0, reset=-60.0, thresh=-52.0, refrac=5,
decay=1e-2, trace_tc=5e-2, theta_plus=theta_plus, theta_decay=theta_decay),
tc_decay=100.0, tc_trace=20.0, theta_plus=theta_plus,
tc_theta_decay=tc_theta_decay),
name='Ae')

self.add_layer(LIFNodes(n=self.n_neurons, traces=False, rest=-60.0, reset=-45.0, thresh=-40.0, decay=1e-1,
refrac=2, trace_tc=5e-2),
self.add_layer(LIFNodes(n=self.n_neurons, traces=False, rest=-60.0, reset=-45.0, thresh=-40.0, tc_decay=10.0,
refrac=2, tc_trace=20.0),
name='Ai')

w = 0.3 * torch.rand(self.n_inpt, self.n_neurons)
self.add_connection(Connection(source=self.layers['X'], target=self.layers['Ae'], w=w, update_rule=PostPre,
nu=nu, wmin=wmin, wmax=wmax, norm=norm, decay=X_Ae_decay),
nu=nu, wmin=wmin, wmax=wmax, norm=norm),
source='X', target='Ae')

w = self.exc * torch.diag(torch.ones(self.n_neurons))
self.add_connection(Connection(source=self.layers['Ae'], target=self.layers['Ai'], w=w, wmin=0, wmax=self.exc,
decay=Ae_Ai_decay),
self.add_connection(Connection(source=self.layers['Ae'], target=self.layers['Ai'], w=w, wmin=0, wmax=self.exc),
source='Ae', target='Ai')

w = -self.inh * (torch.ones(self.n_neurons, self.n_neurons) - torch.diag(torch.ones(self.n_neurons)))
self.add_connection(Connection(source=self.layers['Ai'], target=self.layers['Ae'], w=w, wmin=-self.inh, wmax=0,
decay=Ai_Ae_decay),
self.add_connection(Connection(source=self.layers['Ai'], target=self.layers['Ae'], w=w, wmin=-self.inh, wmax=0),
source='Ai', target='Ae')


Expand All @@ -122,7 +116,7 @@ class DiehlAndCook2015v2(Network):
def __init__(self, n_inpt: int, n_neurons: int = 100, inh: float = 17.5, dt: float = 1.0,
nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2), wmin: Optional[float] = None,
wmax: Optional[float] = None, norm: float = 78.4, theta_plus: float = 0.05,
theta_decay: float = 1e-7) -> None:
tc_theta_decay: float = 1e7) -> None:
# language=rst
"""
Constructor for class ``DiehlAndCook2015v2``.
Expand All @@ -136,7 +130,7 @@ def __init__(self, n_inpt: int, n_neurons: int = 100, inh: float = 17.5, dt: flo
:param wmax: Maximum allowed weight on input to excitatory synapses.
:param norm: Input to excitatory layer connection weights normalization constant.
:param theta_plus: On-spike increment of ``DiehlAndCookNodes`` membrane threshold potential.
:param theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
:param tc_theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
"""
super().__init__(dt=dt)

Expand All @@ -145,12 +139,12 @@ def __init__(self, n_inpt: int, n_neurons: int = 100, inh: float = 17.5, dt: flo
self.inh = inh
self.dt = dt

input_layer = Input(n=self.n_inpt, traces=True, trace_tc=5e-2)
input_layer = Input(n=self.n_inpt, traces=True, tc_trace=20.0)
self.add_layer(input_layer, name='X')

output_layer = DiehlAndCookNodes(
n=self.n_neurons, traces=True, rest=-65.0, reset=-60.0, thresh=-52.0, refrac=5,
decay=1e-2, trace_tc=5e-2, theta_plus=theta_plus, theta_decay=theta_decay
tc_decay=100.0, tc_trace=20.0, theta_plus=theta_plus, tc_theta_decay=tc_theta_decay
)
self.add_layer(output_layer, name='Y')

Expand All @@ -177,7 +171,7 @@ class IncreasingInhibitionNetwork(Network):

def __init__(self, n_input: int, n_neurons: int = 100, start_inhib: float = 1.0, max_inhib: float = 100.0,
dt: float = 1.0, nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2), wmin: float = 0.0,
wmax: float = 1.0, norm: float = 78.4, theta_plus: float = 0.05, theta_decay: float = 1e-7) -> None:
wmax: float = 1.0, norm: float = 78.4, theta_plus: float = 0.05, tc_theta_decay: float = 1e7) -> None:
# language=rst
"""
Constructor for class ``IncreasingInhibitionNetwork``.
Expand All @@ -191,7 +185,7 @@ def __init__(self, n_input: int, n_neurons: int = 100, start_inhib: float = 1.0,
:param wmax: Maximum allowed weight on input to excitatory synapses.
:param norm: Input to excitatory layer connection weights normalization constant.
:param theta_plus: On-spike increment of ``DiehlAndCookNodes`` membrane threshold potential.
:param theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
:param tc_theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
"""
super().__init__(dt=dt)

Expand All @@ -202,12 +196,12 @@ def __init__(self, n_input: int, n_neurons: int = 100, start_inhib: float = 1.0,
self.max_inhib = max_inhib
self.dt = dt

input_layer = Input(n=self.n_input, traces=True, trace_tc=5e-2)
input_layer = Input(n=self.n_input, traces=True, tc_trace=20.0)
self.add_layer(input_layer, name='X')

output_layer = DiehlAndCookNodes(
n=self.n_neurons, traces=True, rest=-65.0, reset=-60.0, thresh=-52.0, refrac=5,
decay=1e-2, trace_tc=5e-2, theta_plus=theta_plus, theta_decay=theta_decay
tc_decay=100.0, tc_trace=20.0, theta_plus=theta_plus, tc_theta_decay=tc_theta_decay
)
self.add_layer(output_layer, name='Y')

Expand Down Expand Up @@ -244,7 +238,7 @@ class LocallyConnectedNetwork(Network):
def __init__(self, n_inpt: int, input_shape: List[int], kernel_size: Union[int, Tuple[int, int]],
stride: Union[int, Tuple[int, int]], n_filters: int, inh: float = 25.0, dt: float = 1.0,
nu: Optional[Union[float, Sequence[float]]] = (1e-4, 1e-2), theta_plus: float = 0.05,
theta_decay: float = 1e-7, wmin: float = 0.0, wmax: float = 1.0, norm: Optional[float] = 0.2,
tc_theta_decay: float = 1e7, wmin: float = 0.0, wmax: float = 1.0, norm: Optional[float] = 0.2,
real=False) -> None:
# language=rst
"""
Expand All @@ -262,7 +256,7 @@ def __init__(self, n_inpt: int, input_shape: List[int], kernel_size: Union[int,
:param wmin: Minimum allowed weight on ``Input`` to ``DiehlAndCookNodes`` synapses.
:param wmax: Maximum allowed weight on ``Input`` to ``DiehlAndCookNodes`` synapses.
:param theta_plus: On-spike increment of ``DiehlAndCookNodes`` membrane threshold potential.
:param theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
:param tc_theta_decay: Time constant of ``DiehlAndCookNodes`` threshold potential decay.
:param norm: ``Input`` to ``DiehlAndCookNodes`` layer connection weights normalization constant.
:param real: Whether to use real-valued (non-spiking) input (implemented as a "clamp").
"""
Expand All @@ -279,7 +273,7 @@ def __init__(self, n_inpt: int, input_shape: List[int], kernel_size: Union[int,
self.inh = inh
self.dt = dt
self.theta_plus = theta_plus
self.theta_decay = theta_decay
self.tc_theta_decay = tc_theta_decay
self.wmin = wmin
self.wmax = wmax
self.norm = norm
Expand All @@ -291,13 +285,13 @@ def __init__(self, n_inpt: int, input_shape: List[int], kernel_size: Union[int,
int((input_shape[1] - kernel_size[1]) / stride[1]) + 1)

if real:
input_layer = RealInput(n=self.n_inpt, traces=True, trace_tc=5e-2)
input_layer = RealInput(n=self.n_inpt, traces=True, tc_trace=20.0)
else:
input_layer = Input(n=self.n_inpt, traces=True, trace_tc=5e-2)
input_layer = Input(n=self.n_inpt, traces=True, tc_trace=20.0)

output_layer = DiehlAndCookNodes(
n=self.n_filters * conv_size[0] * conv_size[1], traces=True, rest=-65.0, reset=-60.0,
thresh=-52.0, refrac=5, decay=1e-2, trace_tc=5e-2, theta_plus=theta_plus, theta_decay=theta_decay
thresh=-52.0, refrac=5, tc_decay=100.0, tc_trace=20.0, theta_plus=theta_plus, tc_theta_decay=tc_theta_decay
)
input_output_conn = LocallyConnectedConnection(
input_layer, output_layer, kernel_size=kernel_size, stride=stride, n_filters=n_filters,
Expand Down

0 comments on commit 0e2bb60

Please sign in to comment.