Daniel L. Yamins

Dr. Daniel Bear propose to augment state-of-the-art neural networks with two biologically-inspired properties: the ability to represent the physical world as it changes over time and the ability to learn from self-created signals rather than explicit human instruction.

Investigating how the brain develops from infancy to adulthood across species, focusing on how the interplay between structural development, functional development, experience and affect brain computations and ultimately behavior.

In the course of everyday functioning, animals (including humans) are constantly faced with real-world environments in which they are required to shift unpredictably between multiple, sometimes unfamiliar, tasks. But how brains support this rapid adaptation of decision making schema, and how they allocate resources towards learning novel tasks is largely unknown both neuroscientifically and algorithmically.