Decision-Making Computations in Neuronal Networks

Recent advances in neuroscience provide a new perspective on how categorical decisions are computed in the brain and how the choice circuits performing such computations are organized. This knowledge was instrumental in developing the mechanistic model of the choice circuit based on the recurrent attractor network of binary neurons. The model produced biologically realistic network dynamics and behaviorally realistic predictions. The model was applied in the investigation of the impact of alpha oscillations on the decision process, speed-accuracy tradeoff (SAT), and Weber's Law. During the seminar, I will present the key results from my doctoral dissertation.