CNSKit

A Python workflow for training task-specific models on a MaleCNS graph and running them in an application: bind observations to chosen body IDs, train a readout, input and output adapters or bounded per-neuron gains, evaluate held-out episodes, and export a pickle-free checkpoint that carries the graph fingerprint, ordered channels and provenance. Matched recurrent, zero-edge and ridge baselines are included. The README declines the usual claims, saying anatomical connectivity is only an experimental prior and that the most useful next result would be a well-controlled study of when it helps.

Official site ↗

#malecns#pytorch#readout-training#checkpoints#baselines