norse.torch.module

norse.torch.module#

Modules for spiking neural network, adhering to the torch.nn.Module interface.

Modules

`coba_lif`
`conv`
`encode`	Stateful encoders as torch modules.
`exp_filter`	Exponential smoothing is rule of thumb techique for smoothing time series data using the exponential window function.
`iaf`
`izhikevich`
`leaky_integrator`	Leaky integrators describe a leaky neuron membrane that integrates incoming currents over time, but never spikes.
`leaky_integrator_box`	Leaky integrators describe a leaky neuron membrane that integrates incoming currents over time, but never spikes.
`lif`	A very popular neuron model that combines a `norse.torch.module.leaky_integrator` with spike thresholds to produce events (spikes).
`lif_adex`
`lif_adex_refrac`
`lif_box`	A simplified version of the popular leaky integrate-and-fire neuron model that combines a `norse.torch.functional.leaky_integrator` with spike thresholds to produce events (spikes).
`lif_correlation`
`lif_ex`
`lif_mc`
`lif_mc_refrac`
`lif_refrac`
`lift`
`lsnn`	Long-short term memory module, building on the work by [G.
`receptive_field`	These receptive fields are derived from scale-space theory, specifically in the paper Normative theory of visual receptive fields by Lindeberg, 2021.
`regularization`	This module contains ``torch.nn.Module``s for regularisation operations on spiking layers, where it can be desirable to regularise spikes, membrane parameters, or other properties over time.
`sequential`
`snn`	Base module for spiking neural network (SNN) modules.
`spikes_to_times_decoder`
`test`