Neural coding and dynamics
Very generally, our group is interested in better understanding neural codes and dynamics, to learn how the brain computes.
Coding: In principle, the brain could encode information about a variable in any of myrid 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, and so on. Understanding a neural code means not only extracting what is coded, but learning the tradeoffs of the encoding scheme, to learn "why" it was selected. Currently, we are studying these questions in the context of song motor coding in songbirds and position coding for navigation in mammals.
Error correction: Representations in the brain are necessarily noisy because of the stochastic dynamics of neurons and synapses. If errors remain they will propagate, which can hinder the accuracy and usefulness of computation. Avoiding such problems requires agressive error reduction and correction, but our understanding of how the brain does this is at best primitive. Population coding, based on averaging, is one approach to reduce error. Instead, we are investigating exact error correcting codes as they may exist in the brain.
Dynamics: What kinds of connectivity patterns are necessary to produce the appropriate output for sequential motor control and neural integration? How robust are such networks to noise and perturbation? What are the developmental and plasticity rules that allow such structures to form? We study these questions through simulation and theoretical investigation of noise, robustness, and learning.