Ila P FieteAssociate Professor
Department of Neuroscience, Department of PhysicsTo better understand the dynamics and coding principles that underlie computation in the firstname.lastname@example.org
The University of Texas at Austin
Department of Neuroscience, College of Natural Sciences
1 University Station C7000
Austin, TX 78712
Ila Fiete is an Associate Professor in the Department of Neuroscience and the Institute for Neuroscience, at UT Austin. She obtained her Ph.D. at Harvard under the guidance of Sebastian Seung at MIT. Her postdoctoral work was at the Kavli Institute for Theoretical Physics at Santa Barbara, and at Caltech, where she was a Broad Fellow. Ila Fiete is a fellow in the Center for Learning and Memory, a McKnight Scholar, and an ONR Young Investigator. She has been an Alfred P. Sloan Foundation Fellow and a Searle Scholar.
To better understand the dynamics and coding principles that underlie computation in the brain.
Our group is interested in better understanding neural codes and dynamics, to learn how the brain computes. Our tools are numerical and theoretical, and our approach is to work closely with collaborators on specific experimental systems.
Coding: In principle, the brain could encode information about a variable in any of myriad ways. The choice of coding scheme sheds light on the computational priorities of the brain in representing that variable. For instance, codes can differ in capacity, ease of readout by downstream areas, or noise tolerance. Understanding a neural code means not only learning what or how much is encoded, but learning the tradeoffs of the coding scheme, to see "why" it was selected.
Error correction: Representations in the brain are necessarily noisy because of the stochastic dynamics of neurons and synapses. Avoiding such problems requires agressive error reduction and correction, but our understanding of how the brain does this is at best primitive. We are investigating strong error correcting codes as they may exist in the brain.
Dynamics of learning and memory: How robust are neural memory networks to ongoing noise? What kinds of network connectivity support integration and memory? How do such structures form through development and plasticity? We study these questions through simulation and theory. We also analyze neural data with a view toward discovering mechanism.
- Fellow in the Center for Learning and Memory
- Alfred P. Sloan Foundation Fellow
- Searle Scholar
- McKnight Scholar
- ONR Young Investigator