MushroomBody_Chess

MushroomBody_Chess v2

Synaptic weights for BeatTheFly -- A Smart Fruit Fly is playing chess against you: a spiking network wired as the real Drosophila mushroom-body connectome that plays chess.

The anatomical connectome gives you wiring, not synaptic strengths. Ours are trained.

Synaptic weights trained with PHCSSM parallel-scan mode, deployment in sequential RSNN mode (PHCSSM).

made by Po-Han Chiang @ NYCU

Architecture

• Wiring: MaleCNS v1.0 mushroom body -- 4,510 neurons (4,064 Kenyon cells, 97 MBONs, 340 DANs, 2 APL, 2 DPM, 5 MB-C1) and 1,027,152 neuron-to-neuron connections. The connectivity mask is fixed to the connectome; 828,479 connections carry a nonzero weight and 0 weights lie off the connectome.
• Dale's law: one sign per presynaptic neuron from neurotransmitter annotations (excitatory 4,114, inhibitory 52, modulatory 344); 0 weights violate it.
• Inputs: each ply provides the move token (one of 1,970 UCI moves) and the board after it (789 binary features: piece per square, castling rights, en-passant file, 50-move-clock buckets, seen from the side to move). Two linear encoders with their own LayerNorm drive the Kenyon cells; the move token also drives the dopaminergic neurons.
• Neurons: leaky integrate-and-fire with per-neuron leak, threshold and reset; synaptic delay of one step.
• Fast weight: dopamine-gated associative memory on the Kenyon-cell -> MBON synapses, read back into the MBON voltages.
• Readout: linear map from the membrane voltage of all neurons to the move vocabulary.
• Deployment: sequential RSNN mode, one timestep per ply, with the neuron state and fast weight carried across the whole game.

Data sources

Data sources: human games from the Lichess open database (lichess.org, CC0); move labels from the Stockfish chess engine.

The previous version remains in this repository's history.

Evaluation

4,000 held-out Lichess games between players rated 2200+, legal moves only:

Human move-match on the same games: 39.1%; on held-out Lichess blitz games (1500–1800): 36.4%.

Strength: ≈1110 Elo (95% CI ±39) vs Stockfish UCI_Elo anchors, CCRL Blitz scale, 800 games, argmax play. +139 Elo over v1 on the same openings (95% CI +75 to +211).

Files

• manifest.json -- every tensor (file, dtype, shape, bytes), the model scalars and a connectome audit.
• info.json -- neuron metadata used by the page (cell classes, hemispheres, soma coordinates).
• selfcheck_<precision>.json -- reference moves and logits that the page replays when it loads.
• chess_uci_vocab.json -- the move vocabulary.
• fp16/, fp32/ -- raw little-endian arrays.

Two precisions are listed in the manifest: fp16w32 (default, 49.5 MB: float16 for the four large dense matrices, float32 for the recurrent weights and all small tensors) and fp16 (47.8 MB, recurrent weights in float16 as well).

The recurrent weight matrix W[dst, src] is stored in CSC order by source neuron (W_colptr, W_rowidx, W_vals): each step multiplies W by a sparse binary spike vector, so the engine visits only the columns of the neurons that spiked. Dense matrices are stored in the orientation they are read: enc_tok_T [vocab, H] (a move token selects one row), enc_brd_T [789, H] (sum of the active rows), dec_w [vocab, H] and v2d_T [vocab, n_dan].

Numerical check: legal top-1 1968/1973 vs the fp32 reference (24 held-out games); 0 of 8,925,290 spike bits differ from the reference on the same weights.

Limitations

There is no search and no evaluation function: each move is a single timestep of the network. It is weakest in the endgame (31.4% agreement with Stockfish's best move).

License and attribution

Weights: CC-BY-NC-4.0. They are derived from the MaleCNS v1.0 connectome (Janelia FlyEM and collaborators, https://male-cns.janelia.org/, CC-BY-4.0) and trained with PHCSSM (https://arxiv.org/abs/2604.01295); please credit both.

Citation

PHCSSM: https://arxiv.org/abs/2604.01295