# -*- coding: utf-8 -*-
import numbers
from typing import Optional, Union, Sequence, Tuple, Callable
import jax.numpy as jnp
from jax import vmap
import brainpy.math as bm
from brainpy._src.dynsys import Projection
from brainpy._src.initialize import Initializer
from brainpy.check import is_sequence
from brainpy.types import ArrayType
__all__ = [
'DelayCoupling',
'DiffusiveCoupling',
'AdditiveCoupling',
]
class DelayCoupling(Projection):
"""Delay coupling.
Parameters
----------
delay_var: Variable
The delay variable.
var_to_output: Variable, sequence of Variable
The target variables to output.
conn_mat: ArrayType
The connection matrix.
required_shape: sequence of int
The required shape of `(pre, post)`.
delay_steps: int, ArrayType
The matrix of delay time steps. Must be int.
initial_delay_data: Initializer, Callable
The initializer of the initial delay data.
"""
def __init__(
self,
delay_var: bm.Variable,
var_to_output: Union[bm.Variable, Sequence[bm.Variable]],
conn_mat: ArrayType,
required_shape: Tuple[int, ...],
delay_steps: Optional[Union[int, ArrayType, Callable]] = None,
initial_delay_data: Union[Callable, ArrayType, numbers.Number] = None,
name: Optional[str] = None,
mode: Optional[bm.Mode] = None,
):
super().__init__(name=name, mode=mode)
# delay variable
if not isinstance(delay_var, bm.Variable):
raise ValueError(f'"delay_var" must be an instance of brainpy.math.Variable. '
f'But we got {type(delay_var)}')
self.delay_var = delay_var
# output variables
if isinstance(var_to_output, bm.Variable):
var_to_output = [var_to_output]
is_sequence(var_to_output, 'output_var', elem_type=bm.Variable, allow_none=False)
self.output_var = var_to_output
# Connection matrix
self.conn_mat = conn_mat
if self.conn_mat.shape != required_shape:
raise ValueError(f'we expect the structural connection matrix has the shape of '
f'(pre.num, post.num), i.e., {required_shape}, '
f'while we got {self.conn_mat.shape}.')
# Delay matrix
if delay_steps is None:
self.delay_steps = None
self.delay_type = 'none'
num_delay_step = None
elif callable(delay_steps):
delay_steps = delay_steps(required_shape)
if delay_steps.dtype not in [jnp.int32, jnp.int64, jnp.uint32, jnp.uint64]:
raise ValueError(f'"delay_steps" must be integer typed. But we got {delay_steps.dtype}')
self.delay_steps = delay_steps
self.delay_type = 'array'
num_delay_step = self.delay_steps.max()
elif isinstance(delay_steps, (bm.Array, jnp.ndarray)):
if delay_steps.dtype not in [jnp.int32, jnp.int64, jnp.uint32, jnp.uint64]:
raise ValueError(f'"delay_steps" must be integer typed. But we got {delay_steps.dtype}')
if delay_steps.ndim == 0:
self.delay_type = 'int'
else:
self.delay_type = 'array'
if delay_steps.shape != required_shape:
raise ValueError(f'we expect the delay matrix has the shape of '
f'(pre.num, post.num), i.e., {required_shape}. '
f'While we got {delay_steps.shape}.')
self.delay_steps = delay_steps
num_delay_step = self.delay_steps.max()
elif isinstance(delay_steps, int):
self.delay_steps = delay_steps
num_delay_step = delay_steps
self.delay_type = 'int'
else:
raise ValueError(f'Unknown type of delay steps: {type(delay_steps)}')
# delay variables
_ = self.register_delay(f'delay_{id(delay_var)}',
delay_step=num_delay_step,
delay_target=delay_var,
initial_delay_data=initial_delay_data)
def reset_state(self, batch_size=None):
pass
[docs]
class DiffusiveCoupling(DelayCoupling):
"""Diffusive coupling.
This class simulates the model of::
coupling = g * (delayed_coupling_var1 - coupling_var2)
target_var += coupling
Examples
--------
>>> import brainpy as bp
>>> from brainpy import rates
>>> areas = bp.rates.FHN(80, x_ou_sigma=0.01, y_ou_sigma=0.01, name='fhn')
>>> conn = bp.synapses.DiffusiveCoupling(areas.x, areas.x, areas.input,
>>> conn_mat=Cmat, delay_steps=Dmat,
>>> initial_delay_data=bp.init.Uniform(0, 0.05))
>>> net = bp.Network(areas, conn)
Parameters
----------
coupling_var1: Variable
The first coupling variable, used for delay.
coupling_var2: Variable
Another coupling variable.
var_to_output: Variable, sequence of Variable
The target variables to output.
conn_mat: ArrayType
The connection matrix.
delay_steps: int, ArrayType
The matrix of delay time steps. Must be int.
initial_delay_data: Initializer, Callable
The initializer of the initial delay data.
name: str
The name of the model.
"""
[docs]
def __init__(
self,
coupling_var1: bm.Variable,
coupling_var2: bm.Variable,
var_to_output: Union[bm.Variable, Sequence[bm.Variable]],
conn_mat: ArrayType,
delay_steps: Optional[Union[int, ArrayType, Initializer, Callable]] = None,
initial_delay_data: Union[Initializer, Callable, ArrayType, float, int, bool] = None,
name: str = None,
mode: bm.Mode = None,
):
if not isinstance(coupling_var1, bm.Variable):
raise ValueError(f'"coupling_var1" must be an instance of brainpy.math.Variable. '
f'But we got {type(coupling_var1)}')
if not isinstance(coupling_var2, bm.Variable):
raise ValueError(f'"coupling_var2" must be an instance of brainpy.math.Variable. '
f'But we got {type(coupling_var2)}')
if jnp.ndim(coupling_var1) != 1:
raise ValueError(f'Only support 1d vector of coupling variable. '
f'But we got {jnp.ndim(coupling_var1)}')
if jnp.ndim(coupling_var2) != 1:
raise ValueError(f'Only support 1d vector of coupling variable. '
f'But we got {jnp.ndim(coupling_var2)}')
super().__init__(
delay_var=coupling_var1,
var_to_output=var_to_output,
conn_mat=conn_mat,
required_shape=(coupling_var1.size, coupling_var2.size),
delay_steps=delay_steps,
initial_delay_data=initial_delay_data,
name=name,
mode=mode,
)
self.coupling_var1 = coupling_var1
self.coupling_var2 = coupling_var2
[docs]
def update(self):
# delays
axis = self.coupling_var1.ndim
delay_var = self.get_delay_var(f'delay_{id(self.delay_var)}')
if self.delay_steps is None:
diffusive = (jnp.expand_dims(self.coupling_var1.value, axis=axis) -
jnp.expand_dims(self.coupling_var2.value, axis=axis - 1))
diffusive = (self.conn_mat * diffusive).sum(axis=axis - 1)
elif self.delay_type == 'array':
if isinstance(self.mode, bm.TrainingMode):
indices = (slice(None, None, None), jnp.arange(self.coupling_var1.size),)
else:
indices = (jnp.arange(self.coupling_var1.size),)
f = vmap(lambda steps: delay_var.retrieve(steps, *indices), in_axes=1) # (..., pre.num)
delays = f(self.delay_steps) # (..., post.num, pre.num)
diffusive = (jnp.moveaxis(bm.as_jax(delays), axis - 1, axis) -
jnp.expand_dims(self.coupling_var2.value, axis=axis - 1)) # (..., pre.num, post.num)
diffusive = (self.conn_mat * diffusive).sum(axis=axis - 1)
elif self.delay_type == 'int':
delayed_data = delay_var.retrieve(self.delay_steps) # (..., pre.num)
diffusive = (jnp.expand_dims(delayed_data, axis=axis) -
jnp.expand_dims(self.coupling_var2.value, axis=axis - 1)) # (..., pre.num, post.num)
diffusive = (self.conn_mat * diffusive).sum(axis=axis - 1)
else:
raise ValueError(f'Unknown delay type {self.delay_type}')
# output to target variable
for target in self.output_var:
target.value += diffusive
[docs]
class AdditiveCoupling(DelayCoupling):
"""Additive coupling.
This class simulates the model of::
coupling = g * delayed_coupling_var
target_var += coupling
Parameters
----------
coupling_var: Variable
The coupling variable, used for delay.
var_to_output: Variable, sequence of Variable
The target variables to output.
conn_mat: ArrayType
The connection matrix.
delay_steps: int, ArrayType
The matrix of delay time steps. Must be int.
initial_delay_data: Initializer, Callable
The initializer of the initial delay data.
name: str
The name of the model.
"""
[docs]
def __init__(
self,
coupling_var: bm.Variable,
var_to_output: Union[bm.Variable, Sequence[bm.Variable]],
conn_mat: ArrayType,
delay_steps: Optional[Union[int, ArrayType, Initializer, Callable]] = None,
initial_delay_data: Union[Initializer, Callable, ArrayType, float, int, bool] = None,
name: str = None,
mode: bm.Mode = None,
):
if not isinstance(coupling_var, bm.Variable):
raise ValueError(f'"coupling_var" must be an instance of brainpy.math.Variable. '
f'But we got {type(coupling_var)}')
if jnp.ndim(coupling_var) != 1:
raise ValueError(f'Only support 1d vector of coupling variable. '
f'But we got {jnp.ndim(coupling_var)}')
super().__init__(
delay_var=coupling_var,
var_to_output=var_to_output,
conn_mat=conn_mat,
required_shape=(coupling_var.size, coupling_var.size),
delay_steps=delay_steps,
initial_delay_data=initial_delay_data,
name=name,
mode=mode,
)
self.coupling_var = coupling_var
[docs]
def update(self):
# delay function
axis = self.coupling_var.ndim
delay_var = self.get_delay_var(f'delay_{id(self.delay_var)}')
if self.delay_steps is None:
additive = self.coupling_var @ self.conn_mat
elif self.delay_type == 'array':
if isinstance(self.mode, bm.TrainingMode):
indices = (slice(None, None, None), jnp.arange(self.coupling_var.size),)
else:
indices = (jnp.arange(self.coupling_var.size),)
f = vmap(lambda steps: delay_var.retrieve(steps, *indices), in_axes=1) # (.., pre.num,)
delays = f(self.delay_steps) # (..., post.num, pre.num)
additive = (self.conn_mat * jnp.moveaxis(delays, axis - 1, axis)).sum(axis=axis - 1)
elif self.delay_type == 'int':
delayed_var = delay_var.retrieve(self.delay_steps) # (..., pre.num)
additive = delayed_var @ self.conn_mat
else:
raise ValueError
# output to target variable
for target in self.output_var:
target.value += additive
