brainpy.synplast.STD#

class brainpy.synplast.STD(tau=200.0, U=0.07, method='exp_auto', name=None)[source]#

Synaptic output with short-term depression.

This model filters the synaptic current by the following equation:

\[I_{syn}^+(t) = I_{syn}^-(t) * x\]

where \(x\) is the normalized variable between 0 and 1, and \(I_{syn}^-(t)\) and \(I_{syn}^+(t)\) are the synaptic currents before and after STD filtering.

Moreover, \(x\) is updated according to the dynamics of:

\[\frac{dx}{dt} = \frac{1-x}{\tau} - U * x * \delta(t-t_{spike})\]

where \(U\) is the fraction of resources used per action potential, \(\tau\) is the time constant of recovery of the synaptic vesicles.

Parameters

tau (float) – The time constant of recovery of the synaptic vesicles.
U (float) – The fraction of resources used per action potential.

See also

STP

__init__(tau=200.0, U=0.07, method='exp_auto', name=None)[source]#

Methods

`__init__`([tau, U, method, name])
`clear_input`()
`clone`()	The function useful to clone a new object when it has been used.
`cpu`()	Move all variable into the CPU device.
`cuda`()	Move all variables into the GPU device.
`filter`(g)
`get_delay_data`(identifier, delay_step, *indices)	Get delay data according to the provided delay steps.
`load_state_dict`(state_dict[, warn])	Copy parameters and buffers from `state_dict` into this module and its descendants.
`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[, node_cls])
`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.
`state_dict`()	Returns a dictionary containing a whole state of the module.
`to`(device)	Moves all variables into the given device.
`tpu`()	Move all variables into the TPU device.
`train_vars`([method, level, include_self])	The shortcut for retrieving all trainable variables.
`tree_flatten`()	Flattens the object as a PyTree.
`tree_unflatten`(aux, dynamic_values)	New in version 2.3.1.
`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`	Global delay data, which stores the delay variables and corresponding delay targets.
`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.