Pehlevan Group

Our research areas are theoretical neuroscience and theory of neural computation in natural and artificial systems.

Latest Publications and Preprints

S. Qin, S. Farashahi, D. Lipshutz, A. M. Sengupta, D. B. Chklovskii, and C. Pehlevan, “Coordinated drift of receptive fields during noisy representation learning,” biorXiv 10.1101/2021.08.30.458264, 2021.

B. Bordelon and C. Pehlevan, “Learning Curves for SGD on Structured Features,” arXiv preprint arXiv:2106.02713, 2021.

A. Canatar, B. Bordelon, and C. Pehlevan, “Out-of-Distribution Generalization in Kernel Regression,” arXiv preprint arXiv:2106.02261, 2021.

J. A. Zavatone-Veth, A. Canatar, and C. Pehlevan, “Asymptotics of representation learning in finite Bayesian neural networks ,” arXiv preprint arXiv:2106.00651, 2021.

J. A. Zavatone-Veth and C. Pehlevan, “Exact priors of finite neural networks,” arXiv preprint arXiv:2104.11734, 2021.

B. Bordelon and C. Pehlevan, “Population Codes Enable Learning from Few Examples By Shaping Inductive Bias,” biorXiv 10.1101/2021.03.30.437743v1, 2021.

J. Zavatone-Veth and C. Pehlevan, “Activation function dependence of the storage capacity of treelike neural networks,” Physical Review E, vol. 103, pp. L020301, 2021.

A. Canatar, B. Bordelon, and C. Pehlevan, “Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks,” Nature Communications, vol. 12, pp. 2914 , 2021.

Affiliations

John A. Paulson School of Engineering and Applied Science

Center for Brain Science

Swartz Program in Theoretical Neuroscience

Harvard Machine Learning Theory

Program in Neuroscience

The Biophysics Program

The Quantitative Biology Initiative

Harvard Brain Science Initiative

The Mind Brain Behavior Interfaculty Initiative

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