brainpy.dyn.synapses.DiffusiveCoupling#

class brainpy.dyn.synapses.DiffusiveCoupling(coupling_var1, coupling_var2, var_to_output, conn_mat, delay_steps=None, initial_delay_data=None, name=None, mode=NormalMode)[source]#

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.dyn 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.dyn.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 (JaxArray, ndarray) – The connection matrix.

  • delay_steps (int, JaxArray, ndarray) – 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.

__init__(coupling_var1, coupling_var2, var_to_output, conn_mat, delay_steps=None, initial_delay_data=None, name=None, mode=NormalMode)[source]#

Methods

__init__(coupling_var1, coupling_var2, ...)

check_post_attrs(*attrs)

Check whether post group satisfies the requirement.

check_pre_attrs(*attrs)

Check whether pre group satisfies the requirement.

clear_input()

get_delay_data(identifier, delay_step, *indices)

Get delay data according to the provided delay steps.

load_states(filename[, verbose])

Load the model states.

nodes([method, level, include_self])

Collect all children nodes.

offline_fit(target, fit_record)

offline_init()

online_fit(target, fit_record)

online_init()

register_delay(identifier, delay_step, ...)

Register delay variable.

register_implicit_nodes(*nodes, **named_nodes)

register_implicit_vars(*variables, ...)

reset([batch_size])

Reset function which reset the whole variables in the model.

reset_local_delays([nodes])

Reset local delay variables.

reset_state([batch_size])

Reset function which reset the states in the model.

save_states(filename[, variables])

Save the model states.

train_vars([method, level, include_self])

The shortcut for retrieving all trainable variables.

unique_name([name, type_])

Get the unique name for this object.

update(tdi)

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

global_delay_data

mode

Mode of the model, which is useful to control the multiple behaviors of the model.

name

Name of the model.