Trends in NeuroAI - Unified Scalable Neural Population Decoding (POYO)
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Paul Scotti
Feb 15, 2024, 9:40:24 AM2/15/24
to MedARC Neuroimaging & AI
Next Thursday Feb 22 11am ET we have lead author Mehdi Azabou presenting his work "A Unified, Scalable Framework for Neural Population Decoding". I highly recommend checking out the beautiful project page for this work here: https://poyo-brain.github.io/. Mehdi is an ML PhD student at Georgia Tech advised by Dr. Eva Dyer. Hope to see you there!
Title:
A Unified, Scalable Framework for Neural Population Decoding
Abstract:
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and 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, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.
Speaker:
Mehdi Azabou (Georgia Institute of Technology)
Paper link: https://arxiv.org/abs/2310.16046
To access the Zoom link you can add the event to your Calendar (Google Calendar | iCal | View in browser)
Or here's the direct Zoom link: https://princeton.zoom.us/j/97664762824
Best,
Paul
A Unified, Scalable Framework for Neural Population Decoding
Abstract:
Our ability to use deep learning approaches to decipher neural activity would likely benefit from greater scale, in terms of both model size and 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, spanning over 158 different sessions of recording from over 27,373 neural units and over 100 hours of recordings. In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale.
Speaker:
Mehdi Azabou (Georgia Institute of Technology)
Paper link: https://arxiv.org/abs/2310.16046
To access the Zoom link you can add the event to your Calendar (Google Calendar | iCal | View in browser)
Or here's the direct Zoom link: https://princeton.zoom.us/j/97664762824
Best,
Paul
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