It this like knowing only this:
which neuron is connected to which neuron
But you don't know:
the values of the weights (the value of the neuron, or the parameters)
the activation functions
what circuit do neurons implement (fully connected? CNN?)
Also, “activation function” isn’t exactly the right thing for real biological neurons. They aren’t just functions of the current input or the like. Their behavior depends on their recent history. Some will like, by themselves iirc, periodically fire. Others will fire if enough input is sent within some amount of time (in some models of some of them there’s like, some accumulations of signal when receiving inputs, which gradually decays/leaks, and it fires (and depletes) if enough is accumulated).
But yes, the idea is that “what is connected to what” is obtained, but not more specific things about how the ones that are connected are connected (how the behavior of one relates to the behavior of the ones it is connected to).
yes. and you can get VERY roughly connection strengths by synapse count but that's as far as you can go
However the real challenge would be:
1. bring this mapping into a AI framework for inferencing 2. We don't know the "OS" on how it runs. Just randomly triggering a neuron probably wouldn't work as there is a lot of other factors that trigger neurons.