  Recent advances to algorithms for training spiking neural networks (SNNs)
often leverage their unique dynamics. While backpropagation through time (BPTT)
with surrogate gradients dominate the field, a rich landscape of alternatives
can situate algorithms across various points in the performance,
bio-plausibility, and complexity landscape. Evaluating and comparing algorithms
is currently a cumbersome and error-prone process, requiring them to be
repeatedly re-implemented. We introduce Slax, a JAX-based library designed to
accelerate SNN algorithm design, compatible with the broader JAX and Flax
ecosystem. Slax provides optimized implementations of diverse training
algorithms, allowing direct performance comparison. Its toolkit includes
methods to visualize and debug algorithms through loss landscapes, gradient
similarities, and other metrics of model behavior during training.
