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Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution

arxiv.org · 9,122 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. Neuroscience research has made immense progress over the last decade, but our understanding of the brain remains fragmented and piecemeal: the dream of probing an arbitrary brain region and automatically reading out the information encoded in its neural activity remains out of reach. In this work, we build towards a first foundation model for neural spiking data that can solve a diverse set of tasks across multiple brain areas. We introduce a novel self-supervised modeling approach for population activity in which the model alternates between masking out and reconstructing neural activity across different time steps, neurons, and brain regions. To evaluate our approach, we design unsupervised and supervised prediction tasks using the International Brain

Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution Yizi Zhang Columbia University New York yz4123@columbia.edu &Yanchen Wang Stanford University California ppwang@stanford.edu &Donato Jiménez Benetó Universitat Politècnica de Catalunya Spain donatojb@mit.edu &Zixuan Wang Columbia University New York zw3008@columbia.edu &Mehdi Azabou Georgia Institute of Technology Georgia mazabou@gatech.edu &Blake Richards Mila, McGill University Montreal blake.richards@mila.quebe &Olivier Winter Champalimaud Foundation Portugal &The International Brain Laboratory The

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