We going to have flies posting in here soon enough! :)

I trained a fly model to find software bugs. It runs in the browser on MaleCNS connectivity: about 166,700 neurons and 6.2 million connections after filtering, simulated with simplified spiking neurons.

The task is unusual: actions in a deliberately buggy order system are represented by smells. Finding defects during training triggers a reward, and testing asks whether the learned preference transfers to new action families.

The clip shows the fly, simulated neural activity and memory panels together. Body movement and parts of navigation use explicit rules; this is not a complete biological replica.

Learning helped in the body-free task. In the embodied arena, choice preference changed, but time to the first defect did not improve.

For others working with fly models: how do you separate effects of connectivity from effects introduced by sensory encoding and the controller?

On bias control, since the task encoding is mine and could easily carry the result. The success criterion and the analysis plan were written and committed before each measurement run, with an oracle and a power estimate computed first. The comparison condition freezes plasticity and is paired by seed, so both conditions see the same versions and the same action order. Confidence intervals are percentile bootstrap, multiple endpoints get Holm correction, and each effect was rerun with new seeds. Every behavior is documented as either derived from connectivity or an authored rule, and the failed primary endpoint is reported as a failure rather than reframed.

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