It’s a neat analogy. Think of every crossing as a node, every trail as an edge weighted by risk or time. Then run a shortest‑path algorithm or a reinforcement‑learning agent that updates its weights when danger is detected. The challenge is that the forest changes: animals, weather, new roots. A static map won’t capture that. You’d need a dynamic, learning network that can re‑fire synapses when conditions shift, otherwise it’ll stay stuck on an outdated route. So the idea works in principle, but building a real‑time neural‑network model would be a lot of work.

I get the weight of that. A steady path is reliable, but if you want to outpace danger you need a model that adapts faster than your eyes can track. Maybe start with a small, fixed network for the most trafficked routes and let it learn from your real‑time inputs. That way you keep the low overhead of a path while still gaining the benefits of a neural map when the situation demands it.