CurrentDerivedSpikeMixin¶

class CurrentDerivedSpikeMixin(currents: Tensor, to_spikes: Callable[[InfernoSynapse, dtype, device, Tensor], Tensor], interp: Interpolation, interp_kwargs: dict[str, Any], current_overbound: float | None, spike_overbound: bool | None, tolerance: float)[source]¶

Mixin for synapses with current and spikes derived therefrom.

Parameters:

currents (torch.Tensor) – initial synaptic currents, in \(\text{nA}\).
to_spikes (Callable[[InfernoSynapse, torch.dtype, torch.device, torch.Tensor], torch.Tensor]) – function which takes the synapse, data type, device, and a tensor of currents, and returns the corresponding spikes.
interp (Interpolation) – interpolation function used when selecting prior currents and spikes derived therefrom.
interp_kwargs (dict[str, Any]) – keyword arguments passed into the interpolation function.
current_overbound (float | None) – value to replace currents out of bounds, uses values at observation limits if None.
spike_overbound (bool | None) – value to replace spikes out of bounds, uses values at observation limits if None.
tolerance (float) – maximum difference in time from an observation to treat as co-occurring, in \(\text{ms}\).

property spike: Tensor¶

Spike input to the synapses at present.

Note

The setter does nothing as spikes are derived from currents.

spike_at(selector: Tensor) → Tensor[source]¶

Retrieves previous spike inputs.

Parameters:: selector (torch.Tensor) – time before present for which spike inputs should be retrieved, in \(\text{ms}\).
Returns:: selected spike inputs.
Return type:: torch.Tensor

Shape

selector:

\(B \times N_0 \times \cdots \times [D]\)

return:

\(B \times N_0 \times \cdots \times [D]\)

Where: