Bagherian, Dawna and Gornet, James and Bernstein, Jeremy and Ni, Yu-Li and Yue, Yisong and Meister, Markus (2021) Fine-Grained System Identification of Nonlinear Neural Circuits. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery , New York, NY, pp. 14-24. ISBN 978-1-4503-8332-5. https://resolver.caltech.edu/CaltechAUTHORS:20210920-171551019
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Abstract
We study the problem of sparse nonlinear model recovery of high dimensional compositional functions. Our study is motivated by emerging opportunities in neuroscience to recover fine-grained models of biological neural circuits using collected measurement data. Guided by available domain knowledge in neuroscience, we explore conditions under which one can recover the underlying biological circuit that generated the training data. Our results suggest insights of both theoretical and practical interests. Most notably, we find that a sign constraint on the weights is a necessary condition for system recovery, which we establish both theoretically with an identifiability guarantee and empirically on simulated biological circuits. We conclude with a case study on retinal ganglion cell circuits using data collected from mouse retina, showcasing the practical potential of this approach.
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| Additional Information: | © 2021 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1745301. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This work was also funded by a cloud computing grant from Amazon Web Services in collaboration with the Information Science and Technology initiative at Caltech. This work was also supported by the Simons Collaboration on the Global Brain (grant 543015 to Markus Meister). Jeremy Bernstein was supported in part by an NVIDIA Fellowship and by NASA TRISH-RFA-BRASH 1901. Yu-Li Ni was supported by Taipei Veterans General Hospital-National Yang-Ming University Excellent Physician Scientists Cultivation Program, No. 103-Y-A-003. The authors thank James Parkin for providing original illustrations of retinal neurons. | ||||||||||||||
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| Subject Keywords: | nonlinear system identification, neural networks, neuroscience | ||||||||||||||
| DOI: | 10.1145/3447548.3467402 | ||||||||||||||
| Record Number: | CaltechAUTHORS:20210920-171551019 | ||||||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20210920-171551019 | ||||||||||||||
| Official Citation: | Dawna Bagherian, James Gornet, Jeremy Bernstein, Yu-Li Ni, Yisong Yue, and Markus Meister. 2021. Fine-Grained System Identification of Nonlinear Neural Circuits. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’21), August 14–18, 2021, Virtual Event, Singapore. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3447548.3467402 | ||||||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||||||
| ID Code: | 110961 | ||||||||||||||
| Collection: | CaltechAUTHORS | ||||||||||||||
| Deposited By: | Tony Diaz | ||||||||||||||
| Deposited On: | 20 Sep 2021 18:00 | ||||||||||||||
| Last Modified: | 20 Sep 2021 18:00 |
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