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:
tau (
Union
[float
,TypeVar
(ArrayType
,Array
,Variable
,TrainVar
,Array
,ndarray
),Callable
]) – float, ArrayType, Callable. The time constant of recovery of the synaptic vesicles.U (
Union
[float
,TypeVar
(ArrayType
,Array
,Variable
,TrainVar
,Array
,ndarray
),Callable
]) – float, ArrayType, Callable. The fraction of resources used per action potential.size (
Union
[int
,Sequence
[int
]]) – int, or sequence of int. The neuronal population size.keep_size (
bool
) – bool. Keep the neuron group size.