Training shapes the curvature of shallow neural network representations,” in NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022.

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

## Latest Publications and Preprints

Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?” in NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations (Oral), 2022.

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Dynamical Mean Field Theory of Kernel Evolution in Wide Neural Networks,” in NeurIPS 2022 workshop on Machine Learning and the Physical Sciences, 2022.

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Contrasting random and learned features in deep Bayesian linear regression,” in NeurIPS 2022 workshop on Machine Learning and the Physical Sciences, 2022.

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Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation,” arXiv preprint arXiv:2210.04222, 2022.

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A Kernel Analysis of Feature Learning in Deep Neural Networks,” 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton). 2022.

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The Influence of Learning Rule on Representation Dynamics in Wide Neural Networks,” arXiv preprint arXiv:2210.02157, 2022.

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Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation ,” arXiv preprint arXiv:2209.10634, 2022.

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