Pehlevan Group

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

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Latest Publications and Preprints

J. A. Zavatone-Veth, A. Canatar, B. S. Ruben, and C. Pehlevan, “Asymptotics of representation learning in finite Bayesian neural networks,” J Stat Mech: Theory and Experiments (accepted for publication), 2022.

A. Canatar, E. Peters, C. Pehlevan, S. M. Wild, and R. Shaydulin, “Bandwidth Enables Generalization in Quantum Kernel Models ,” arXiv preprint arXiv:2206.06686 , 2022.

P. Masset*, J. A. Zavatone-Veth*, P. J. Connor, V. Murthy#, and C. Pehlevan#, “Population geometry enables fast sampling in spiking neural networks,” biorXiv 10.1101/2022.06.03.494680, 2022.

A. Lin, et al., “Functional imaging and quantification of multi-neuronal olfactory responses in C. elegans,” biorXiv 10.1101/2022.05.27.493772, 2022.

B. Bordelon and C. Pehlevan, “Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks ,” arXiv preprint arXiv:2205.09653, 2022.

J. A. Zavatone-Veth, W. L. Tong, and C. Pehlevan, “Contrasting random and learned features in deep Bayesian linear regression,” Physical Review E, vol. 105, no. 6, pp. 064118, 2022.

J. A. Zavatone-Veth and C. Pehlevan, “On neural network kernels and the storage capacity problem,” Neural Computation, vol. 34, no. 5, pp. 1136-1142, 2022.

A. Atanasov*, B. Bordelon*, and C. Pehlevan, “Neural Networks as Kernel Learners: The Silent Alignment Effect,” in International Conference on Learning Representations (ICLR), 2022.

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