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Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing

View ORCID ProfileMarcus A. Triplett, View ORCID ProfileMarta Gajowa, View ORCID ProfileBenjamin Antin, View ORCID ProfileMasato Sadahiro, View ORCID ProfileHillel Adesnik, View ORCID ProfileLiam Paninski
doi: https://doi.org/10.1101/2022.09.14.507926
Marcus A. Triplett
1Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
2Grossman Center for the Statistics of Mind, Columbia University, NY
3Center for Theoretical Neuroscience, Columbia University, NY
4Department of Statistics, Columbia University, NY
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  • ORCID record for Marcus A. Triplett
  • For correspondence: marcus.triplett@columbia.edu
Marta Gajowa
5Department of Molecular and Cell Biology, University of California, Berkeley, CA
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Benjamin Antin
1Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
2Grossman Center for the Statistics of Mind, Columbia University, NY
3Center for Theoretical Neuroscience, Columbia University, NY
4Department of Statistics, Columbia University, NY
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Masato Sadahiro
5Department of Molecular and Cell Biology, University of California, Berkeley, CA
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Hillel Adesnik
5Department of Molecular and Cell Biology, University of California, Berkeley, CA
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Liam Paninski
1Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY
2Grossman Center for the Statistics of Mind, Columbia University, NY
3Center for Theoretical Neuroscience, Columbia University, NY
4Department of Statistics, Columbia University, NY
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Abstract

Discovering how neural computations are implemented in the cortex at the level of monosynaptic connectivity requires probing for the existence of synapses from possibly thousands of presynaptic candidate neurons. Two-photon optogenetics has been shown to be a promising technology for mapping such monosynaptic connections via serial stimulation of neurons with single-cell resolution. However, this approach is limited in its ability to uncover connectivity at large scales because stimulating neurons one-by-one requires prohibitively long experiments. Here we developed novel computational tools that, when combined, enable learning of monosynaptic connectivity from high-speed holographic neural ensemble stimulation. First, we developed a model-based compressed sensing algorithm that identifies connections from postsynaptic responses evoked by stimulation of many neurons at once, considerably increasing the rate at which the existence and strength of synapses are screened. Second, we developed a deep learning method that isolates the postsynaptic response evoked by each stimulus, allowing stimulation to rapidly switch between ensembles without waiting for the postsynaptic response to return to baseline. Together, our system increases the throughput of monosynaptic connectivity mapping by an order of magnitude over existing approaches, enabling the acquisition of connectivity maps at speeds needed to discover the synaptic circuitry implementing neural computations.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/marcustriplett/circuitmap

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 September 17, 2022.
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Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing
Marcus A. Triplett, Marta Gajowa, Benjamin Antin, Masato Sadahiro, Hillel Adesnik, Liam Paninski
bioRxiv 2022.09.14.507926; doi: https://doi.org/10.1101/2022.09.14.507926
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Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing
Marcus A. Triplett, Marta Gajowa, Benjamin Antin, Masato Sadahiro, Hillel Adesnik, Liam Paninski
bioRxiv 2022.09.14.507926; doi: https://doi.org/10.1101/2022.09.14.507926

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