My research areas are theoretical neuroscience and theory of neural computation in natural and artificial systems. We pursue various research directions in theoretical neuroscience and neuroscience-guided machine learning, including theories of population coding in the brain, theory of deep learning, biologically plausible learning algorithms, representation learning in the brain, theory of olfactory processing, and computation in spiking neural networks.

We seek to uncover the algorithms of the brain and their implementation at the network and cellular levels. But, how can we infer what the brain computes from the large datasets of modern neuroscience? For that, we need a new “algorithmic” theory that bridges computation and its biological realization.

We have been working on an algorithmic theory for learning in the sensory domain. Sensory cortices learn from stimuli to build behaviorally relevant representations, with little or no supervision. Our theory starts by posing computational goals of unsupervised learning as mathematical optimization problems. Then, from these problems, we systematically derive algorithms and neural circuit implementations of these algorithms, linking computation to biological realization. Our approach answers how efficient self-organization for learning happens with local synaptic plasticity, provides new circuit motifs and mechanisms that could be in use in the brain, predicts computations performed in specific circuits, and provides computational interpretations of salient features of such circuits.

The described theory is a path to neuroscience-guided machine learning. The neural algorithms we uncovered, so far, solve unsupervised learning tasks such as linear dimensionality reduction, sparse and/or nonnegative feature extraction, blind nonnegative source separation, clustering and manifold learning. Many of these algorithms are on par in performance with state-of-the-art machine learning.

We have broad interests in theoretical neuroscience, and actively look for collaborations with experimentalists and projects motivated by new experimental results. This led us to propose a model of how the songbird brain might recover from lesions and a neuromechanical model of the fly larva‘s crawling. We studied reinforcement learning of motor skill timing to infer how the brain solves this computationally hard problem at the network level. We figured out how stimulus selectivity could emerge in a network with random connectivity.

My group is also involved in various collaborations with experimentalists who are all affiliates of the biophysics program. 1) With Venki Murthy, we examine how the olfactory cortex combines and coordinates information from both sides of the brain and how it represents olfactory objects . 2) With Bence Olveczky, we are studying the hierarchical structure of behavior using videos of freely moving rats and deep unsupervised learning. 3) With Aravi Samuel, Paul Sternberg (Caltech) and Mei Zhen (Toronto), we use a multitude of modeling methods to uncover the mechanisms behind Dauer decision making in C. elegans. 4) With Aravi Samuel, we investigate how serotonin changes the internal state of olfactory circuits and behavior in Drosophila larvae.