brainpy.dyn.base.SynConn#

class brainpy.dyn.base.SynConn(pre, post, conn=None, name=None, mode=NormalMode)[source]#

Base class to model two-end synaptic connections.

Parameters
  • pre (NeuGroup) – Pre-synaptic neuron group.

  • post (NeuGroup) – Post-synaptic neuron group.

  • conn (optional, ndarray, JaxArray, dict, TwoEndConnector) – The connection method between pre- and post-synaptic groups.

  • name (str, optional) – The name of the dynamic system.

__init__(pre, post, conn=None, name=None, mode=NormalMode)[source]#

Methods

__init__(pre, post[, conn, name, mode])

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[, pre_spike])

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.