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Binary and analog variation of synapses between cortical pyramidal neurons

View ORCID ProfileSven Dorkenwald, View ORCID ProfileNicholas L. Turner, View ORCID ProfileThomas Macrina, Kisuk Lee, Ran Lu, View ORCID ProfileJingpeng Wu, Agnes L. Bodor, View ORCID ProfileAdam A. Bleckert, View ORCID ProfileDerrick Brittain, Nico Kemnitz, View ORCID ProfileWilliam M. Silversmith, Dodam Ih, Jonathan Zung, Aleksandar Zlateski, Ignacio Tartavull, Szi-Chieh Yu, Sergiy Popovych, William Wong, Manuel Castro, Chris S. Jordan, Alyssa M. Wilson, Emmanouil Froudarakis, JoAnn Buchanan, View ORCID ProfileMarc Takeno, View ORCID ProfileRussel Torres, Gayathri Mahalingam, View ORCID ProfileForrest Collman, View ORCID ProfileCasey Schneider-Mizell, Daniel J. Bumbarger, Yang Li, Lynne Becker, Shelby Suckow, View ORCID ProfileJacob Reimer, View ORCID ProfileAndreas S. Tolias, View ORCID ProfileNuno Maçarico da Costa, View ORCID ProfileR. Clay Reid, View ORCID ProfileH. Sebastian Seung
doi: https://doi.org/10.1101/2019.12.29.890319
Sven Dorkenwald
Computer Science Department, Princeton University, Princeton, NJ, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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  • For correspondence: svenmd@princeton.edu sseung@princeton.edu
Nicholas L. Turner
Computer Science Department, Princeton University, Princeton, NJ, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Thomas Macrina
Computer Science Department, Princeton University, Princeton, NJ, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Kisuk Lee
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USABrain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
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Ran Lu
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Jingpeng Wu
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Agnes L. Bodor
Allen Institute for Brain Science, Seattle, WA, USA
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Adam A. Bleckert
Allen Institute for Brain Science, Seattle, WA, USA
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Derrick Brittain
Allen Institute for Brain Science, Seattle, WA, USA
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Nico Kemnitz
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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William M. Silversmith
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Dodam Ih
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Jonathan Zung
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Aleksandar Zlateski
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Ignacio Tartavull
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Szi-Chieh Yu
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Sergiy Popovych
Computer Science Department, Princeton University, Princeton, NJ, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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William Wong
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Manuel Castro
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Chris S. Jordan
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Alyssa M. Wilson
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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Emmanouil Froudarakis
Department of Neuroscience, Baylor College of Medicine, Houston, TX, USACenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
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JoAnn Buchanan
Allen Institute for Brain Science, Seattle, WA, USA
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Marc Takeno
Allen Institute for Brain Science, Seattle, WA, USA
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Russel Torres
Allen Institute for Brain Science, Seattle, WA, USA
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Gayathri Mahalingam
Allen Institute for Brain Science, Seattle, WA, USA
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Forrest Collman
Allen Institute for Brain Science, Seattle, WA, USA
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Casey Schneider-Mizell
Allen Institute for Brain Science, Seattle, WA, USA
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Daniel J. Bumbarger
Allen Institute for Brain Science, Seattle, WA, USA
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Yang Li
Allen Institute for Brain Science, Seattle, WA, USA
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Lynne Becker
Allen Institute for Brain Science, Seattle, WA, USA
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Shelby Suckow
Allen Institute for Brain Science, Seattle, WA, USA
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Jacob Reimer
Department of Neuroscience, Baylor College of Medicine, Houston, TX, USACenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA
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Andreas S. Tolias
Department of Neuroscience, Baylor College of Medicine, Houston, TX, USACenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USADepartment of Electrical and Computer Engineering, Rice University, Houston, TX, USA
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Nuno Maçarico da Costa
Allen Institute for Brain Science, Seattle, WA, USA
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R. Clay Reid
Allen Institute for Brain Science, Seattle, WA, USA
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H. Sebastian Seung
Computer Science Department, Princeton University, Princeton, NJ, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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  • For correspondence: svenmd@princeton.edu sseung@princeton.edu
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Abstract

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L2/3 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al., 2007) by a log-normal distribution (Loewenstein, Kuras and Rumpel, 2011; de Vivo et al., 2017; Santuy et al., 2018). A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris, 1993; Koester and Johnston, 2005; Bartol et al., 2015; Kasthuri et al., 2015; Dvorkin and Ziv, 2016; Bloss et al., 2018; Motta et al., 2019). We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma.

Footnotes

  • AAB (TissueVision), AZ (Facebook AI Research), EF (Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology Hellas)

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted December 31, 2019.
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Binary and analog variation of synapses between cortical pyramidal neurons
Sven Dorkenwald, Nicholas L. Turner, Thomas Macrina, Kisuk Lee, Ran Lu, Jingpeng Wu, Agnes L. Bodor, Adam A. Bleckert, Derrick Brittain, Nico Kemnitz, William M. Silversmith, Dodam Ih, Jonathan Zung, Aleksandar Zlateski, Ignacio Tartavull, Szi-Chieh Yu, Sergiy Popovych, William Wong, Manuel Castro, Chris S. Jordan, Alyssa M. Wilson, Emmanouil Froudarakis, JoAnn Buchanan, Marc Takeno, Russel Torres, Gayathri Mahalingam, Forrest Collman, Casey Schneider-Mizell, Daniel J. Bumbarger, Yang Li, Lynne Becker, Shelby Suckow, Jacob Reimer, Andreas S. Tolias, Nuno Maçarico da Costa, R. Clay Reid, H. Sebastian Seung
bioRxiv 2019.12.29.890319; doi: https://doi.org/10.1101/2019.12.29.890319
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Binary and analog variation of synapses between cortical pyramidal neurons
Sven Dorkenwald, Nicholas L. Turner, Thomas Macrina, Kisuk Lee, Ran Lu, Jingpeng Wu, Agnes L. Bodor, Adam A. Bleckert, Derrick Brittain, Nico Kemnitz, William M. Silversmith, Dodam Ih, Jonathan Zung, Aleksandar Zlateski, Ignacio Tartavull, Szi-Chieh Yu, Sergiy Popovych, William Wong, Manuel Castro, Chris S. Jordan, Alyssa M. Wilson, Emmanouil Froudarakis, JoAnn Buchanan, Marc Takeno, Russel Torres, Gayathri Mahalingam, Forrest Collman, Casey Schneider-Mizell, Daniel J. Bumbarger, Yang Li, Lynne Becker, Shelby Suckow, Jacob Reimer, Andreas S. Tolias, Nuno Maçarico da Costa, R. Clay Reid, H. Sebastian Seung
bioRxiv 2019.12.29.890319; doi: https://doi.org/10.1101/2019.12.29.890319

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