How I trained a simulated fruit fly to find software bugs, shown next to its neural activity

[u/J0NN4SS]

I trained a simulated fruit fly to find bugs in software. This is the fly exploring the arena, with the simulated spikes and its memory shown beside it: a 3D body, a simplified spiking-neuron model based on MaleCNS connectivity, and live panels.

The arena plaques represent actions in a synthetic software-testing task. They emit modeled odors; discovering a planted defect during training produces a reward signal.

The neural display shows spikes from the running model. Locomotion and parts of navigation use authored rules, so the movement should not be read as an entirely emergent biological behavior.

This is a short, sped-up interface clip. It shows how the components run together rather than demonstrating a learning advantage on its own.

Is the connection between the fly's action and the neural/memory views readable, or would a single view with occasional close-ups make the simulation easier to follow?

A note on how it was validated, since a simulation like this is easy to make look convincing. Every behavior in the project is labeled as either coming from the connectivity data or being a rule I wrote. For the learning claims, the success criterion was written and committed before each measurement run, the comparison is a frozen-learning control paired by seed, the statistics use bootstrap confidence intervals with Holm correction across endpoints, and every effect was replicated with new seeds. The negative results stayed in the writeup.

Project and documentation: https://github.com/JonnasFigueiredo/mosca-qa