STD

STD#

class brainpy.state.STD(in_size, name=None, tau=Quantity(200., 'ms'), U=0.07)#

Synapse with short-term depression.

This class implements a synapse model with short-term depression (STD), which captures activity-dependent reduction in synaptic efficacy, typically caused by depletion of neurotransmitter vesicles following repeated stimulation.

The model is characterized by the following equation:

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

\[ g_{syn} = x \]

where:

  • \(x\) represents the available synaptic resources (depression variable)

  • \(\tau\) is the depression recovery time constant

  • \(U\) is the utilization parameter (fraction of resources depleted per spike)

  • \(\delta(t - t_{spike})\) is the Dirac delta function representing presynaptic spikes

  • \(g_{syn}\) is the effective synaptic conductance

Parameters:

  • in_size (Size) – Size of the input.

  • name (str, optional) – Name of the synapse instance.

  • tau (ArrayLike, default 200.*u.ms) – Time constant governing recovery of synaptic resources in milliseconds.

  • U (ArrayLike, default 0.07) – Utilization parameter (fraction of resources used per action potential).

x#

Available synaptic resources (depression variable).

Type:

HiddenState

See also

STP

Full short-term plasticity model with facilitation and depression.

Notes

  • Larger values of tau lead to slower recovery from depression [1].

  • Larger values of U cause stronger depression with each spike.

  • This model is a simplified version of the STP model that only includes depression [2].

References

Examples

>>> import brainpy
>>> import brainstate
>>> import saiunit as u
>>> # Create an STD synapse
>>> std = brainpy.state.STD(100, tau=200.*u.ms, U=0.07)
>>> std.init_state(batch_size=1)
init_state(batch_size=None, **kwargs)[source]#

State initialization function.

reset_state(batch_size=None, **kwargs)[source]#

State resetting function.