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Representation learning for neural population activity with Neural Data Transformers

View ORCID ProfileJoel Ye, View ORCID ProfileChethan Pandarinath
doi: https://doi.org/10.1101/2021.01.16.426955
Joel Ye
1Georgia Institute of Technology
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  • For correspondence: joelye9@gmail.com
Chethan Pandarinath
1Georgia Institute of Technology
2Emory University
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Abstract

Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using recurrence to explicitly model dynamics necessitates sequential processing of data, slowing real-time applications such as brain-computer interfaces. Here we introduce the Neural Data Transformer (NDT), a non-recurrent alternative. We test the NDT’s ability to capture autonomous dynamical systems by applying it to synthetic datasets with known dynamics and data from monkey motor cortex during a reaching task well-modeled by RNNs. The NDT models these datasets as well as state-of-the-art recurrent models. Further, its non-recurrence enables 3.9ms inference, well within the loop time of real-time applications and more than 6 times faster than recurrent baselines on the monkey reaching dataset. These results suggest that an explicit dynamics model is not necessary to model autonomous neural population dynamics.

Code github.com/snel-repo/neural-data-transformers.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • joel.ye{at}gatech.edu, chethan{at}gatech.edu

  • Self-attention clarified, contributions added.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 21, 2021.
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Representation learning for neural population activity with Neural Data Transformers
Joel Ye, Chethan Pandarinath
bioRxiv 2021.01.16.426955; doi: https://doi.org/10.1101/2021.01.16.426955
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Representation learning for neural population activity with Neural Data Transformers
Joel Ye, Chethan Pandarinath
bioRxiv 2021.01.16.426955; doi: https://doi.org/10.1101/2021.01.16.426955

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