Pehlevan Group

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

Latest Publications and Preprints

J. A. Zavatone-Veth and C. Pehlevan, “Learning curves for deep structured Gaussian feature models ,” arXiv preprint arXiv:2303.00564, 2023, 2023.

J. A. Zavatone-Veth, S. Yang*, J. A. Rubinfien*, and C. Pehlevan, “Neural networks learn to magnify areas near decision boundaries,” arXiv preprint arXiv:2301.11375, 2023.

A. Atanasov*, B. Bordelon*, S. Sainathan, and C. Pehlevan, “The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes,” International Conference on Learning Representations (ICLR), 2023.

S. Qin, S. Farashahi*, D. Lipshutz*, A. Sengupta, D. B. Chklovskii, and C. Pehlevan, “Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning,” Nature Neuroscience , 2023.

B. Bozkurt, A. Isfendiyaroglu, C. Pehlevan, and A. T. Erdogan, “Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation,” International Conference on Learning Representations (ICLR), 2023.

B. Bordelon and C. Pehlevan, “The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks,” International Conference on Learning Representations (ICLR) (notable-top-25%), 2023.

D. Lipshutz, C. Pehlevan, and D. B. Chklovskii, “Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation,” International Conference on Learning Representations (ICLR), 2023.

A. Lin, et al., “Functional imaging and quantification of multi-neuronal olfactory responses in C. elegans,” Science Advances , vol. 9, no. 9, pp. eade1249, 2023.

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