Developer Simulates Fruit Fly Connectome in Python to Play Tic-Tac-Toe

A developer built a tic-tac-toe AI using a fruit fly connectome model comprising roughly 130,000 leaky-integrate-and-fire neurons and five million synapses, running entirely on 8 GB of RAM in Python. The initial implementation failed within 50 simulation ticks due to out-of-memory errors caused by unbounded Python lists, per-instance dictionaries on every neuron object, and excessive garbage collection pauses. Key fixes included adopting __slots__ and NumPy float32 buffers for neuron storage, replacing spike lists with fixed-capacity deque ring buffers, and switching synapse data to cache-friendly array structures. A two-phase tick architecture was also introduced to correctly separate spike propagation from voltage integration, preventing nondeterministic self-feedback artifacts. These changes reduced peak memory usage from approximately 6.2 GB to around 1.8 GB, allowing the simulation to run stably and feed valid moves to a minimax safety layer for final game decisions.