brainpy.dyn.synapses.GapJunction#

class brainpy.dyn.synapses.GapJunction(pre, post, conn, comp_method='dense', g_max=1.0, name=None)[source]#
__init__(pre, post, conn, comp_method='dense', g_max=1.0, name=None)[source]#

Methods

__init__(pre, post, conn[, comp_method, ...])

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.

init_weights(weight, comp_method[, sparse_data])

rtype

Union[float, TypeVar(Array, JaxArray, Variable, TrainVar, Array, ndarray)]

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.

syn2post_with_all2all(syn_value, syn_weight)

syn2post_with_dense(syn_value, syn_weight, ...)

syn2post_with_one2one(syn_value, syn_weight)

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.