# Resources

## Primary Sources

- [SuperSpike paper (Zenke & Ganguli 2018)](https://doi.org/10.1162/neco_a_01086) — The learning rule implemented in the SNN path. Three-factor rule: eligibility trace × error signal.
- [Tank Royale API docs](https://robocode-dev.github.io/tank-royale/) — Bot API, game physics, event model.
- [Leaky Integrate-and-Fire model](https://neuronaldynamics.epfl.ch/online/Ch1.S3.html) — The neuron model used (EPFL textbook, free online).

## Codebase

- `SNNBot_garage/src/SNNBot.nim` — Main bot with SNN and reservoir paths
- `SNNBot_garage/src/reservoir.nim` — Grid accumulator (current active path)
- `SNNBot_garage/tests/test_bullet_economy.nim` — Automated battle test

## To Explore

- Hebbian learning / STDP for binary spikes
- Hyperdimensional computing for control tasks
- `SNNBot_garage/research/binary-snn-learning.md` — Research notes (if exists)