One of the persistent challenges in contemporary neuroscience research involves understanding how neurons, through their activity and interactions, perform complex computations. A central question in this regard is: how do we form representations about the world around us? It is an important question not only because it allows us to better gauge how the brain functions, but also because it allows us to develop new, efficient computational algorithms. In this context, the central arc of my doctoral research is the development of modeling paradigms embedded in optimization theory, in order to investigate neural population dynamics and information coding in the brain and to construct new engineering solution approaches via algorithm design.
My research comprises of two parts:
1. Formulation of an optimization based framework to study brain dynamics and functions.
In the fi rst part, we begin by drawing on formulations from optimal control theory to understand what is the functional relevance of observed neural activity patterns in specifi c brain regions. We have found that sensory responses are designed to minimize unnecessary and wasteful activation. It turns out that the theoretical model predictions agree with observations in actual experiments, which we are able to substantiate through experimental collaborations working with two model organisms of differing complexity (i.e., locusts and C. elegans).
2. Formulation of neuroscience inspired models for engineering problems.
In the second part, we leveraged our learnings from biological networks towards design of network based control laws that has engineering significance. In this part of the study we investigated how networks should behave when they need to solve engineering problems with incomplete and mathematically complex information about the world.
Highlights
