I like the overall idea. Cross-disciplinary research like this is essential, and it's usually how many technological advances happen anyway. Nature has always been one of the best sources of inspiration for engineering.
My main feedback is that the pitch spends a lot of time on the motivation and philosophy (which is compelling), but leaves the reader with a fuzzy picture of what the system actually is. I came away understanding why you think this matters, but not with a clear sense of what you've built or are building. For example, what do the distinct regions in your network do? How do they communicate? What does the architecture look like? I'd lead with more of the Snake results, the sparse reward learning, and compute efficiency are your strongest selling points, and give us the architecture, even at a high level, so people can evaluate the technical substance alongside the vision.
Also, the cargo cult reference might not land with everyone. I might just be out of the loop, but maybe consider briefly explaining it or using a more accessible analogy.
Separate question: Are you familiar with the doom-neuron project (https://github.com/SeanCole02/doom-neuron)? They're using actual biological neurons as the compute substrate for playing Doom. Obviously, a different approach from yours, wetware vs. software-simulated brain principles, but it's in a similar orbit of bridging neuroscience and AI. Curious if you see any overlap or lessons from that work?