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Rishu’s FlyCNS-TicTacToe project routes a tic-tac-toe board through a selected circuit from the male fruit fly connectome. With its default safety setting enabled, minimax first identifies the game-theoretically optimal moves; the circuit then picks among them. The result is a programming experiment—not a fly brain independently learning or solving the game.
How does the project turn a board into a move?
FlyCNS-TicTacToe uses a subset of the male Drosophila central nervous system connectome. Its repository identifies the source dataset as male-cns:v1.0, hosted through neuPrint. The extraction pipeline selects descending neurons and upstream central-brain intrinsic neurons, ranks candidate partners by synaptic weight, groups outputs into nine board-cell readouts, and retains the largest connected component of the selected circuit.
The game layer divides selected central-brain neurons into nine input groups, one for each square. A player’s mark sends a positive signal to its group; the opponent’s mark sends a negative signal; an empty square contributes no signal. A simplified leaky-integrator calculation propagates activation over signed connectome edges. The strongest output group corresponding to an empty square becomes the circuit’s preferred move. The repository lists neuprint-python, networkx, and pandas among its tools.
Where do the signs come from?
The project assigns edge signs using database-predicted neurotransmitter identities: acetylcholine is treated as excitatory, GABA and glutamate as inhibitory, and other predictions as neutral. These are predicted identities, not certain measurements of each neuron’s effect, so the signed circuit is an engineering interpretation of connectome metadata.
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Does the connectome actually choose the move?
In the default unbeatable=True configuration, exact minimax determines the set of game-theoretically optimal moves, and the connectome selects among that set. Minimax supplies the strategic guarantee; the circuit acts as a tie-breaker and may have no choice when only one optimal move is available. The project does not demonstrate that the connectome itself can independently play optimally.
Set unbeatable=False to let the circuit choose without minimax filtering. In that mode it can make a losing move. The available project descriptions provide no win-rate benchmark, so neither mode should be presented with an invented performance statistic.
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What happens on an empty board?
With no marks, every input signal is zero and the resulting activation is flat. The repository says the tie is broken by the output group’s input weight, making the opening deterministic. Rishu reports that identical boards produce identical moves, but a likely preference for particular squares has not been measured; there is no documented square-bias result to report.
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Which parts are biological, and which are designed?
The neuron identities, connectivity, synapse counts, and predicted transmitter metadata come from the connectome source, according to the project author. The board encoding, the assignment of neurons to nine cell groups, and the interpretation of circuit outputs as square choices are project conventions. The selected subgraph is not a real sensory pathway that feeds a board into a fly: no fly sees or responds to a tic-tac-toe grid.
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The computation is also not a biophysical simulation of a living fly. It uses leaky-integrator signal propagation and does not model spikes, real-time dynamics, biological time constants, or refractory periods. It is best understood as a way to experiment with connectome-derived wiring inside a deliberately constructed game interface.
How can you run the project?
The repository documents a workflow using uv, a neuPrint token, the neuPrint CNS host, and the male-cns:v1.0 dataset. Service access requirements and repository instructions can change, so consult the linked project and neuPrint pages for current details.
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- Install the project dependencies with
uv sync. - Configure a neuPrint token. The author says a free token is required; check neuPrint for current access requirements.
- Set the neuPrint host to
neuprint-cns.janelia.organd select themale-cns:v1.0dataset as described in the repository. - Run the repository’s extraction pipeline to build the selected circuit.
- Start the terminal game using the repository’s documented command.
The repository identifies the project but does not give the exact extraction or game-start commands, so use the current README rather than guessing command names.
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What licenses apply?
The repository states that its code is MIT licensed, while the connectome data is separately licensed CC-BY. Those licenses cover different materials: the code license does not replace the attribution terms for the dataset. The repository directs users publishing outputs derived from the data to credit the dataset creators separately; check the relevant license text and repository guidance for the precise attribution required.
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What the experiment does—and does not—show
The project makes a biological wiring dataset part of a playable programming experiment. It does not show that a fly can recognize a board, that the selected circuit evolved for tic-tac-toe, or that a biological brain was modeled in realistic detail. With minimax enabled, the game-theoretic safety comes from the conventional algorithm; the connectome-derived circuit contributes only the choice among moves minimax has already judged optimal.
Quick Recap
Sources: FlyCNS-TicTacToe repository; neuPrint.
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