fly-self-driving

The measured MaleCNS adjacency is run as a leaky recurrent rate network: every neuron holds a persistent state updated as state <- (1 - leak) * state + leak * tanh(W @ state + drive), with a per-neuron leak bounded to 0.05-0.95, four graph updates per 50 ms decision, and - unlike the flyhard recipe it forks - the state deliberately carried across decisions because 'a real fly never resets'. That is a simplified leaky unit with stated dynamics, so L1. Not L2: the per-edge gains and per-neuron leaks are genuinely parameters on connectome edges, but the only thing that writes them is torch.optim.Adam through loss.backward() in an offline training script (behaviour cloning then three DAgger rounds, about 35 minutes on one H100), after which driving runs under policy.eval() and torch.no_grad(). No weight changes while the fly drives, and the update is an imitation-learning gradient, not a rule grounded in fly biology - the source disclaims the biology explicitly ('transmitter/receptor biology is not asserted by this model'). Worth crediting: the evaluation includes a randomly rewired graph control, and the README reports where the shuffled control nearly matches.