By Anna Levina (Tübingen, Germany).

It is widely believed that some neuronal states are better suited for computations than others. For example, closeness to the critical state at the second-order phase transition was shown to be particularly suitable for computations in artificial systems. Alternatively, the balance of excitation and inhibition was also associated, i.e., with optimized information encoding. But how can these states be selected, reached, and maintained in the everchanging and dynamic neuronal populations? I will discuss how neuronal systems can maintain the preferred state of balanced excitation and inhibition using structural changes and adaptation. Moreover, flexible adjustment of the state can be used to optimize computation. I present indications of this process in the recordings from the visual cortex of the monkeys during a spatial attention task.

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