STD

class brainpy.dyn.STD(size, keep_size=False, sharding=None, method='exp_auto', name=None, mode=None, tau=200.0, U=0.07)

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 (Union[float, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Callable]) – The time constant of recovery of the synaptic vesicles.

• U (Union[float, TypeVar(ArrayType, Array, Variable, TrainVar, Array, ndarray), Callable]) – The fraction of resources used per action potential.

• size (Union[int, Sequence[int]]) – The neuronal population size.

• sharding (Optional[Sequence[str]]) – The sharding strategy.

• keep_size (bool) – Keep the neuron group size.

• mode (Optional[Mode]) – The computing mode.

• name (Optional[str]) – The group name.

update(pre_spike)

The function to specify the updating rule.