# norse.torch.functional.encode¶

Stateless encoding functionality for Norse, offering different ways to convert numerical inputs to the spiking domain. Note that some functions, like population_encode does not return spikes, but rather numerical values that will have to be converted into spikes via, for instance, the poisson encoder.

Functions

 constant_current_lif_encode(input_current, ...) Encodes input currents as fixed (constant) voltage currents, and simulates the spikes that occur during a number of timesteps/iterations (seq_length). euclidean_distance(x, y) Simple euclidean distance metric. gaussian_rbf(tensor[, sigma]) A gaussian radial basis kernel that calculates the radial basis given a distance value (distance between $$x$$ and a data value $$x'$$, or $$\|\mathbf{x} - \mathbf{x'}\|^2$$ below). poisson_encode(input_values, seq_length[, ...]) Encodes a tensor of input values, which are assumed to be in the range [0,1] into a tensor of one dimension higher of binary values, which represent input spikes. poisson_encode_step(input_values[, f_max, dt]) Encodes a tensor of input values, which are assumed to be in the range [0,1] into a tensor of binary values, which represent input spikes. population_encode(input_values, out_features) Encodes a set of input values into population codes, such that each singular input value is represented by a list of numbers (typically calculated by a radial basis kernel), whose length is equal to the out_features. signed_poisson_encode(input_values, seq_length) Encodes a tensor of input values, which are assumed to be in the range [-1,1] into a tensor of one dimension higher of binary values, which represent input spikes. signed_poisson_encode_step(input_values[, ...]) Creates a poisson distributed signed spike vector, when spike_latency_encode(input_spikes) For all neurons, remove all but the first spike. spike_latency_lif_encode(input_current, ...) Encodes an input value by the time the first spike occurs.