brainpy.dyn.synouts.CUBA#

class brainpy.dyn.synouts.CUBA(target_var='input', name=None)[source]#

Current-based synaptic output.

Given the conductance, this model outputs the post-synaptic current with a identity function:

\[I_{\mathrm{syn}}(t) = g_{\mathrm{syn}}(t)\]
Parameters

name (str) – The model name.

See also

COBA

__init__(target_var='input', name=None)[source]#

Methods

__init__([target_var, name])

clear_input()

clone()

The function useful to clone a new object when it has been used.

filter(g)

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, ...)

register_master(master)

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

isregistered

State of the component, representing whether it has been registered.

mode

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

name

Name of the model.