Spike Encoding: Rate, Temporal & Population Coding

Neuromorphic hardware and spiking neural networks never move a plain floating-point number between units — every value has to be turned into a train of discrete spikes first. This simulator takes one continuous input signal and encodes it live, side by side, with the three encoding schemes used across real neuromorphic systems: rate coding (spike frequency carries the value), temporal/latency coding (spike timing carries the value), and population coding (which neuron in a tuned bank is most active carries the value). Three animated 3D spike rasters update in real time as spikes are generated from Poisson-style and tuning-curve statistics, while live readouts decode each scheme's estimate of the true signal and track its error and spike-rate cost — making the classic accuracy-vs-efficiency tradeoff between the schemes directly visible rather than just described.