We have a new a new snnTorch tutorial/notebook on population coding.

Biologically, the average neuronal firing rate is roughly 0.1-1Hz, which is far slower than the reaction response time of animals and humans.

But if we pool together multiple neurons and count their spikes together, then it becomes possible to measure a firing rate for a population of neurons in a very short window of time.

As it turns out, population codes are also a handy trick for deep learning. Having more output neurons provides far more ‘pathways’ for errors to backprop through.

It also lets us take a more ‘parallel’ approach to training SNNs by swapping sequential steps through time for matrix-vector mults.

Here, we run through using a large pool of output neurons (instead of just the usual ~10 output neurons) to obtain decent results in 1 single simulated time-step.

Link to the tutorial here.

Population Codes in SNNs