What is fly-brain-snntorch?

Fly Brain snnTorch is a simulation of a fruit fly's brain, rebuilt using a Python tool called snnTorch instead of an older tool called Brian2 that a separate research project originally used. In simple terms, this project models how a tiny insect brain reacts to different senses, and it does so with code that runs faster than the original version it is based on. The simulation currently supports four types of sensory input: taste (like sugar), smell, vision, and sound. The author plans to add five more senses in the future, including humidity, temperature, touch, and detecting pheromones and electrical fields. You can choose which sense to simulate by changing one setting in the main Python file, and the results come out as charts stored in the repository's images folder. Running it requires Python along with a set of libraries such as PyTorch (a framework for building and running neural network style computations) and the snnTorch library itself. The README gives separate step by step setup instructions for Linux, Windows, and macOS, using either Conda or Python's built in virtual environment tool, so no prior machine learning experience is assumed for getting it running, just comfort with a terminal. A notable claim in the README is that this new version runs the brain simulation about 68 times faster on a laptop CPU than the original Brian2 based version, based on the author's own timing tests. The project also compares itself honestly to the older approach, noting some technical tradeoffs, such as missing a certain type of neuron behavior that the original method modeled more accurately, along with proposed ways to fix that gap later. The underlying brain connectivity data comes from other open research repositories, released under permissive licenses like MIT and Apache, and the author is careful to note the data was chosen so it would not conflict with those licensing terms. This project would mainly interest researchers and hobbyists curious about neuromorphic computing or biologically inspired AI models, rather than someone looking for a general purpose tool. The full README is longer than what was shown.