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A fruit-fly connectome can supply wiring data for a tic-tac-toe experiment, but it does not make the fly brain play the game. In the accessible FlyCNS-TicTacToe project account, the author maps selected neuron groups to board squares and uses a simple signal-flow calculation; minimax provides strategic move selection in the described unbeatable mode. The mapping and game rules are the experimenter’s design, not a discovered biological decision circuit.
What the project connects
FlyCNS-TicTacToe is described as an experiment inspired by a simple question: what if a small piece of a fruit fly’s nervous system were used to play tic-tac-toe? The project draws on connectome data: a map of neurons and their connections. Its author says the selected cells include neurons sending commands out of the fly brain and central-brain neurons that feed into them, using connection data and synapse counts.
That biological wiring is an input, not a ready-made game-playing module. The author hand-selects groups of neurons and assigns them to the nine board squares. The connectome does not contain tic-tac-toe squares or a built-in rule for what counts as a winning move.
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How the board-to-neuron mapping works
Board positions become neuron groups
Each of the nine squares is associated with a chosen neuron group. The project then uses connectivity and synapse counts to calculate signals through that selected network. This is a constructed representation of board positions: it should not be mistaken for evidence that those neurons naturally encode tic-tac-toe or equivalent choices in a fly.
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The model is signal flow, not a spiking brain simulation
The accessible project account describes a simple signal-flow calculation. It does not model neurons firing spikes or reproduce real neural timing. The author also says the selected neurons are not “the fly’s decision circuit.” Those limits matter: the project is a computational demonstration using connectome-derived structure, not a simulation of a fly consciously considering game moves.
What minimax does—and what it does not show
In the described unbeatable mode, minimax supplies strategic move selection. Minimax searches possible game outcomes and chooses a move that avoids a loss when a draw or win is available. The circuit’s role is different: it can break ties among strategically best moves, rather than independently planning the game.
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Without look-ahead, the circuit can make poor choices. As the project author puts it, “The fly circuit has no look-ahead, so on its own it walks into forks and forgets to block.” Therefore, an unbeatable result in the assisted mode demonstrates the combination of the game-search algorithm and the circuit’s role in selecting among candidate moves; it does not establish that the connectome itself plays optimally.
How to interpret the biological claims
The project account describes neurotransmitter-based excitation and inhibition as predictions. Treat the signs assigned to connections as inferred rather than certain biological facts unless supported by direct evidence. Likewise, a mapping chosen for a game experiment should not be presented as a validated explanation of how the fly brain makes decisions.
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Why two descriptions of the architecture should not be merged
A search-result excerpt for Muhammad Hammad’s “Architectural Breakdown” describes a different-sounding implementation: a spiking simulation with 130,000 leaky-integrate-and-fire neurons and roughly five million synapses, run under an 8 GB RAM ceiling. It reports reducing peak memory use from 6.2 GB to about 1.8 GB and says a minimax safety net corrected approximately 20 percent of decoded moves. These are figures reported in that article excerpt, not independently verified measurements.
Those claims do not match the accessible FlyCNS-TicTacToe account’s description of a non-spiking signal-flow calculation in which minimax chooses among best moves. The available information does not establish that the two accounts document the same implementation. Treat the spiking architecture and its memory and correction figures as claims belonging to Hammad’s article, not as facts about the accessible project account.
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Licensing and reproducing the project
The accessible project account says its code is MIT licensed, its data is CC-BY, and access requires a free neuPrint token. These are the author’s stated terms; check the current repository and data-provider terms before reusing code or data, since licenses and access requirements can change.
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