Quick disclaimer, I did use AI to articulate my Idea, so you will see traces of that in the text. I'm not good at bringing an idea into word form.
The Big Idea / TL;DR
Scientists have completely mapped the brain of an adult fruit fly. We know exactly where all 140,000 neurons are and how they connect. Right now, anyone with a high-end gaming GPU can simulate this entire brain in real-time.
Instead of just running this static simulation, here is the experiment: What if we treat this fruit fly brain as a starting seed, turn on learning rules, and slowly add extra neurons to see how it grows and changes?
This isn't about building a super-intelligent AI out of a fly. It is a controlled sandbox experiment to see how a real, biological brain architecture handles being expanded beyond its natural limits.
How the Experiment Works
Most large-scale brain simulations either use completely random wiring at a massive scale, or they stick strictly to copying a real animal's exact size. This project sits right in the middle:
Start with the Real Fly: Build a digital version of the fruit fly brain using the exact biological map.
Turn on Learning: Give the connections the ability to strengthen, weaken, or change based on activity (plasticity).
Add Extra Space: Slowly inject blank, new neurons into the network, or allow new connections to form.
See How it Reacts: Put the simulated brain into a simple digital environment (like a basic virtual world where it receives sensory inputs and controls movement) and watch how the new parts of the brain organize themselves.
What We Are Trying to Find Out
Stability vs. Chaos: What learning rules keep the expanding brain stable? Do some rules cause the brain to overload into a digital seizure, or cause the network to completely shut down?
Accidental Evolution: Will the extra neurons naturally band together to form new, specialized processing units? Or will they just get tangled up in the old fly pathways?
Identity: How much of the original "fruit fly behavior" stays intact, and how much gets overwritten by the new growth?
The Break Point: What actual limits do we hit when we push a biological design past what evolution intended?
Is This Feasible?
Hardware: You don't need a massive supercomputer to start. A multi-GPU workstation is enough to handle the baseline fly brain and modest amounts of growth. You'd only need serious cloud compute if you wanted to scale it up to several times its original size or run hundreds of tests at once.
Scope: You don't need to scale this up to human or monkey brain sizes to get amazing data. Even growing the fly brain to 2x or 3x its original size would show us how biological networks handle expansion.
Open Questions for Anyone Ready to Build This
I am much better at the conceptual side of this than the actual coding and testing. If you have the hardware and want to run with this idea, here are the immediate puzzles to solve:
The Learning Rules: What specific mathematical rules should dictate how neurons connect so the brain doesn't instantly break or die?
Measuring Success: How do we actually measure "interesting growth" versus just random digital noise?
How to Add Neurons: How should new cells be introduced? Do we drop them in randomly, clone existing types of fly neurons, or try to mimic how brains naturally grow in embryos?
The Sandbox: What kind of simple virtual environment or body should we give this brain so it has something to interact with and learn from?
If this sounds like a project you'd want to build or experiment with, feel free to take this concept, change it, or start running the tests. I'd love to see what actually happens.