I've recently joined the "Control Lab" at the university of Cambridge, but I'll still be researching neuroscience. This is a fanciful attempt to summarize the current dogma in neuroscience about how things like "free will" and "homeostasis" might relate to technical concepts like "optimal control". None of this is new; it is mostly motivated by old ideas of cybernetics, as best explored by Todorov and colleagues.
Computation in the brain employs predictive negative feedback. This inverts (i.e. controls) a system by cancelling a prediction error (residual). Learning rules employ this on slow timescales, consolidating these inverse representations into feed-forward circuits. Recurrent dynamics complement this, by generating the spiking output required to cancel any error not cancelled by the forward network. In turn, these recurrent dynamics refine the forward weights and reduce the amount of feedback needed in the future (assuming the environment is stationary). This then bring us back to the general idea of learning, perception, and control. Todorov's papers are a good treatment of this.
