DiffusiveCoupling

class brainpy.dyn.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.

update()

The function to specify the updating rule.