brainpy.synapses.DiffusiveCoupling

class brainpy.synapses.DiffusiveCoupling(coupling_var1, coupling_var2, var_to_output, conn_mat, delay_steps=None, initial_delay_data=None, name=None, mode=None)

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 (Union[Variable, Sequence[Variable]]) – The target variables to output.

• conn_mat (TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray)) – The connection matrix.

• delay_steps (Union[int, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Initializer, Callable, None]) – The matrix of delay time steps. Must be int.

• initial_delay_data (Union[Initializer, Callable, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), float, int, bool]) – The initializer of the initial delay data.

• name (str) – The name of the model.

__init__(coupling_var1, coupling_var2, var_to_output, conn_mat, delay_steps=None, initial_delay_data=None, name=None, mode=None)

Methods

__init__(coupling_var1, coupling_var2, ...)
add_aft_update(key, fun)	Add the after update into this node
add_bef_update(key, fun)	Add the before update into this node
add_inp_fun(key, fun[, label, category])	Add an input function.
clear_input(*args, **kwargs)	Empty function of clearing inputs.
cpu()	Move all variable into the CPU device.
cuda()	Move all variables into the GPU device.
get_aft_update(key)	Get the after update of this node by the given key.
get_bef_update(key)	Get the before update of this node by the given key.
get_delay_data(identifier, delay_pos, *indices)	Get delay data according to the provided delay steps.
get_delay_var(name)
get_inp_fun(key)	Get the input function.
get_local_delay(var_name, delay_name)	Get the delay at the given identifier (name).
has_aft_update(key)	Whether this node has the after update of the given key.
has_bef_update(key)	Whether this node has the before update of the given key.
jit_step_run(i, *args, **kwargs)	The jitted step function for running.
load_state(state_dict, **kwargs)	Load states from a dictionary.
load_state_dict(state_dict[, warn, compatible])	Copy parameters and buffers from state_dict into this module and its descendants.
nodes([method, level, include_self])	Collect all children nodes.
register_delay(identifier, delay_step, ...)	Register delay variable.
register_implicit_nodes(*nodes[, node_cls])
register_implicit_vars(*variables[, var_cls])
register_local_delay(var_name, delay_name[, ...])	Register local relay at the given delay time.
reset(*args, **kwargs)	Reset function which reset the whole variables in the model (including its children models).
reset_local_delays([nodes])	Reset local delay variables.
reset_state([batch_size])
save_state(**kwargs)	Save states as a dictionary.
setattr(key, value)
state_dict(**kwargs)	Returns a dictionary containing a whole state of the module.
step_run(i, *args, **kwargs)	The step run function.
sum_current_inputs(*args[, init, label])	Summarize all current inputs by the defined input functions .current_inputs.
sum_delta_inputs(*args[, init, label])	Summarize all delta inputs by the defined input functions .delta_inputs.
sum_inputs(*args, **kwargs)
to(device)	Moves all variables into the given device.
tpu()	Move all variables into the TPU device.
tracing_variable(name, init, shape[, ...])	Initialize a variable that can be traced during computations and transformations.
train_vars([method, level, include_self])	The shortcut for retrieving all trainable variables.
tree_flatten()	Flattens the object as a PyTree.
tree_unflatten(aux, dynamic_values)	Unflatten the data to construct an object of this class.
unique_name([name, type_])	Get the unique name for this object.
update()	The function to specify the updating rule.
update_local_delays([nodes])	Update local delay variables.
vars([method, level, include_self, ...])	Collect all variables in this node and the children nodes.

Attributes

after_updates
before_updates
cur_inputs
current_inputs
delta_inputs
implicit_nodes
implicit_vars
mode	Mode of the model, which is useful to control the multiple behaviors of the model.
name	Name of the model.
supported_modes	Supported computing modes.