Binary and analog variation of synapses between cortical pyramidal neurons
- Princeton University, United States
- Allen Institute for Brain Science, United States
- Baylor College of Medicine, United States
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
Emmanouil Froudarakis
Casey M Schneider-Mizell
Nuno Macarico da Costa
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.
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Binary and analog variation of synapses between cortical pyramidal neurons
eLife 11:e76120.
https://doi.org/10.7554/eLife.76120
Further reading
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- Neuroscience
When holding visual information temporarily in working memory (WM), the neural representation of the memorandum is distributed across various cortical regions, including visual and frontal cortices. However, the role of stimulus representation in visual and frontal cortices during WM has been controversial. Here, we tested the hypothesis that stimulus representation persists in the frontal cortex to facilitate flexible control demands in WM. During functional MRI, participants flexibly switched between simple WM maintenance of visual stimulus or more complex rule-based categorization of maintained stimulus on a trial-by-trial basis. Our results demonstrated enhanced stimulus representation in the frontal cortex that tracked demands for active WM control and enhanced stimulus representation in the visual cortex that tracked demands for precise WM maintenance. This differential frontal stimulus representation traded off with the newly-generated category representation with varying control demands. Simulation using multi-module recurrent neural networks replicated human neural patterns when stimulus information was preserved for network readout. Altogether, these findings help reconcile the long-standing debate in WM research, and provide empirical and computational evidence that flexible stimulus representation in the frontal cortex during WM serves as a potential neural coding scheme to accommodate the ever-changing environment.
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- Neuroscience
Human-specific cognitive abilities depend on information processing in the cerebral cortex, where the neurons are significantly larger and their processes longer and sparser compared to rodents. We found that, in synaptically connected layer 2/3 pyramidal cells (L2/3 PCs), the delay in signal propagation from soma to soma is similar in humans and rodents. To compensate for the longer processes of neurons, membrane potential changes in human axons and/or dendrites must propagate faster. Axonal and dendritic recordings show that the propagation speed of action potentials (APs) is similar in human and rat axons, but the forward propagation of excitatory postsynaptic potentials (EPSPs) and the backward propagation of APs are 26 and 47% faster in human dendrites, respectively. Experimentally-based detailed biophysical models have shown that the key factor responsible for the accelerated EPSP propagation in human cortical dendrites is the large conductance load imposed at the soma by the large basal dendritic tree. Additionally, larger dendritic diameters and differences in cable and ion channel properties in humans contribute to enhanced signal propagation. Our integrative experimental and modeling study provides new insights into the scaling rules that help maintain information processing speed albeit the large and sparse neurons in the human cortex.
