Prof. Elad Schneidman

We study the design and function of networks of neurons, networks of brains, and other biological and non-biological networks - asking how they represent and process information, develop, learn, and make decisions.

We combine theoretical work, modeling, analysis of lots of data, and behavioral experiments, in studying neural encoding and decoding in large neural populations, neural circuit architecture, connectomes, deep and shallow neural networks, collective behavior in animal groups and artificial agents, noise and information in biological systems, learning, and decision making.

Exploring questions at the intersection of biology, physics, computation, and learning - we borrow, mix, and design tools and ideas from machine learning, statistical physics, information theory, network theory, applied math in studying learning networks.