Controlling balanced cortical dynamics on slow and fast time scales. (360G-Wellcome-100000_Z_12_Z)

This project studies large neuronal networks that can produce accurate representations of their input, while maintaining the ability to manipulate their internal dynamics, and thus filter out or enhance specific facets of the input. The separation of important and non-important stimuli is essential for all living organisms, but little is known about the synaptic architecture that supports such processing. In the recent past, the balance of excitatory and inhibitory synaptic currents has emerged as a powerful mechanism for signal separation, or gating . In this scheme, an excitatory signal is always delivered in parallel with an inhibitory anti-signal so that the net effect is zero, unless the balance is purposely disturbed to enhance specific signal aspects. Further, inhibitory plasticity has been shown to establish such a balance of excitation and inhibition. Here, I will study how excitatory and inhibitory plasticity can interact to create networks that automatically generate comp lete representations of their input and autonomously enhance important signal facets, a fundamental step towards understanding the functional architecture underlying any thought process. To calibrate our model and confirm its clinical importance, I will collaborate with experimentalists working on similar question in animals, and investigate the neural basis of balanced processing in humans.