Pavel is a postdoctoral researcher at Tatiana Engel’s Lab, reverse-engineering how recurrent neural networks solve neuroscience tasks, explaining their dynamics by concise circuit models. After all, if we can’t understand what simple RNNs are doing, how can we understand the brain? He received his masters at the department of Physics, Moscow State University, Russia, and did his PhD at University of Melbourne, Australia. He is also interested in biological aspects of reinforcement learning, matching the algorithmic functioning to the circuitry of the basal ganglia. 

In his free time, he enjoys reading all sorts of books. His recent favorites are “Broken Money” by Lyn Adlen, “A Brief History of Intelligence” by Max Bennet, “Determined” by Robert Sapolsky.