System Identification in Neural Circuitry. How big is the problem, how do you make it feasible?
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Every aspect of modern science relies upon creating representations of things. And when we do, we pick the signals that interest us and the behavior that interests us. From that, we determine how to interpret the way input is converted into output in a system. Our description of that process is our understanding of the system. The same is true for mental processes and reverse engineering their implementation in neural circuitry.
The feasible approach to this is called (whole) brain emulation and relies on determining precisely which signals we care about and then breaking the problem down into a collection of smaller system identification problems. To tackle those, there is a roadmap that includes structural scanning (connectomics) as well as new tools for functional recording – some of which are now in development in collaboration with laboratories at MIT and Harvard. The scale of the endeavor is examined, considering such specific approaches to system identification in neural circuitry.