class brainpy.dyn.Delta(size, keep_size=False, sharding=None, name=None, mode=None)[source]#

Delta synapse model.

Model Descriptions

The single exponential decay synapse model assumes the release of neurotransmitter, its diffusion across the cleft, the receptor binding, and channel opening all happen very quickly, so that the channels instantaneously jump from the closed to the open state. Therefore, its expression is given by

\[g_{\mathrm{syn}}(t)=g_{\mathrm{max}} e^{-\left(t-t_{0}\right) / \tau}\]

where \(\tau_{delay}\) is the time constant of the synaptic state decay, \(t_0\) is the time of the pre-synaptic spike, \(g_{\mathrm{max}}\) is the maximal conductance.

Accordingly, the differential form of the exponential synapse is given by

\[\begin{aligned} & \frac{d g}{d t} = -\frac{g}{\tau_{decay}}+\sum_{k} \delta(t-t_{j}^{k}). \end{aligned}\]
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 for details.


The function to specify the updating rule.