PiN Faculty Member - Cengiz Pehlevan, PhD

Cengiz Pehlevan, PhD

Assistant Professor of Applied Mathematics

Harvard University
John A. Paulson School of Engineering and Applied Sciences
Pierce Hall room 315, 29 Oxford Street
Cambridge, MA 02138
Tel: 617-998-1811
Email: cpehlevan@seas.harvard.edu
Visit my lab page here.



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.

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.



Last Update: 1/31/2019



Publications

For a complete listing of publications click here.

 


 



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