STD#

class brainpy.dyn.STD(size, keep_size=False, sharding=None, method='exp_auto', name=None, mode=None, tau=200.0, U=0.07)[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:
reset_state(batch_or_mode=None, **kwargs)[source]#

Reset function which resets local states in this model.

Simply speaking, this function should implement the logic of resetting of local variables in this node.

See https://brainpy.readthedocs.io/en/latest/tutorial_toolbox/state_resetting.html for details.

update(pre_spike)[source]#

The function to specify the updating rule.