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POYO-1

poyo-brain.github.io · 2,856 words · saved by 4 readers

Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both the model size and the datasets. However, the integration of many neural recordings into one unified model is challenging, as each recording contains the activity of different neurons from different individual animals. In this paper, we introduce a training framework and architecture designed to model the population dynamics of neural activity across diverse, large-scale neural recordings. Our method first tokenizes individual spikes within the dataset to build an efficient representation of neural events that captures the fine temporal structure of neural activity. We then employ cross-attention and a PerceiverIO backbone to further construct a latent tokenization of neural population activities. Utilizing this architecture and training framework, we construct a large- scale multi-session model trained on large datasets from seven nonhuman primates, spannin

POYO-1 POYO-1 A Unified, Scalable Framework for Neural Population Decoding Mehdi Azabou 1 Vinam Arora 1 Venkataramana Ganesh 1 Ximeng Mao 2,3 Santosh Nachimuthu 1 Michael J. Mendelson 1 Blake Richards 2,4,5 Matthew G. Perich 2,3 Guillaume Lajoie 2,3,5 Eva L. Dyer 1 1 Georgia Institute of Technology, 2 Mila, 3 Université de Montréal, 4 McGill University, 5 CIFAR Paper Code Contents Abstract Datasets & Challenges Data Heterogeneity --> Tokenization --> Tokenization Architecture Pretraining Finetuning Abstract Our ability to use deep learning approaches to decipher neural activity would likely be

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