Our research areas are theoretical neuroscience and neuroscience-guided machine learning. 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.