Binary and analog variation of synapses between cortical pyramidal neurons
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 in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250×140×90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. 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 . 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 such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.
Data availability
All data acquired and produced for this project are available on https://www.microns-explorer.org/phase1
Article and author information
Author details
Funding
Intelligence Advanced Research Projects Activity (D16PC00003)
- 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 M Takeno
- Russel Torres
- Gayathri Mahalingam
- Forrest Collman
- Casey M Schneider-Mizell
- Daniel J Bumbarger
- Yang Li
- Lynne Becker
- Shelby Suckow
- Jacob Reimer
- Andreas Savas Tolias
- Nuno Macarico da Costa
- R Clay Reid
- H Sebastian Seung
G. Harold and Leila Y. Mathers Foundation
- H Sebastian Seung
Intelligence Advanced Research Projects Activity (D16PC00004)
- 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 M Takeno
- Russel Torres
- Gayathri Mahalingam
- Forrest Collman
- Casey M Schneider-Mizell
- Daniel J Bumbarger
- Yang Li
- Lynne Becker
- Shelby Suckow
- Jacob Reimer
- Andreas Savas Tolias
- Nuno Macarico da Costa
- R Clay Reid
- H Sebastian Seung
Intelligence Advanced Research Projects Activity (D16PC00005)
- 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 M Takeno
- Russel Torres
- Gayathri Mahalingam
- Forrest Collman
- Casey M Schneider-Mizell
- Daniel J Bumbarger
- Yang Li
- Lynne Becker
- Shelby Suckow
- Jacob Reimer
- Andreas Savas Tolias
- Nuno Macarico da Costa
- R Clay Reid
- H Sebastian Seung
National Institute of Neurological Disorders and Stroke (U19 NS104648)
- H Sebastian Seung
Army Research Office (W911NF-12-1-0594)
- H Sebastian Seung
National Eye Institute (R01 EY027036)
- H Sebastian Seung
National Institute of Mental Health (U01 MH114824)
- H Sebastian Seung
National Institute of Neurological Disorders and Stroke (R01 NS104926)
- H Sebastian Seung
National Institute of Mental Health (RF1MH117815)
- H Sebastian Seung
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All animal procedures were approved by the Institutional Animal Care and Use Committee at the Allen Institute for Brain Science (1503 and 1804) or Baylor College of Medicine (AN-4703).
Copyright
© 2022, Dorkenwald et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,816
- views
-
- 378
- downloads
-
- 43
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.
-
- Neuroscience
Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows genome-scale imaging of RNAs in individual cells in intact tissues. To date, MERFISH has been applied to image thin-tissue samples of ~10 µm thickness. Here, we present a thick-tissue three-dimensional (3D) MERFISH imaging method, which uses confocal microscopy for optical sectioning, deep learning for increasing imaging speed and quality, as well as sample preparation and imaging protocol optimized for thick samples. We demonstrated 3D MERFISH on mouse brain tissue sections of up to 200 µm thickness with high detection efficiency and accuracy. We anticipate that 3D thick-tissue MERFISH imaging will broaden the scope of questions that can be addressed by spatial genomics.