Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A Biophysical Basis for Learning and Transmitting Sensory Predictions

Salomon Z. Muller, LF Abbott, View ORCID ProfileNathaniel B. Sawtell
doi: https://doi.org/10.1101/2022.10.31.514538
Salomon Z. Muller
1Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
LF Abbott
1Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027
2Department of Physiology and Cellular Biophysics, Columbia University, New York, NY 10027
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nathaniel B. Sawtell
1Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nathaniel B. Sawtell
  • For correspondence: ns2635@columbia.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Homeostatic (anti-Hebbian) forms of synaptic are effective at eliminating “prediction errors” that signal the differences between predicted and actual sensory input. However, such mechanisms appear to preclude the possibility of transmitting the resulting predictions to downstream circuits, severely limiting their utility. Using modeling and recordings from the electrosensory lobe of mormyrid fish, we reveal interactions between axonal and dendritic spikes that support both the learning and transmission of predictions. We find that sensory input modulates the rate of dendritic spikes by adjusting the amplitude of backpropagating axonal action potentials. Homeostatic plasticity counteracts these effects through changes in the underlying membrane potential, allowing the dendritic spike rate to be restored to equilibrium while simultaneously transmitting predictions through modulation of the axonal spike rate. These results reveal how two types of spikes dramatically enhance the computational power of single neurons in support of an ethologically relevant multi-layer computation.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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 4.0 International license.
Back to top
PreviousNext
Posted November 01, 2022.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A Biophysical Basis for Learning and Transmitting Sensory Predictions
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A Biophysical Basis for Learning and Transmitting Sensory Predictions
Salomon Z. Muller, LF Abbott, Nathaniel B. Sawtell
bioRxiv 2022.10.31.514538; doi: https://doi.org/10.1101/2022.10.31.514538
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A Biophysical Basis for Learning and Transmitting Sensory Predictions
Salomon Z. Muller, LF Abbott, Nathaniel B. Sawtell
bioRxiv 2022.10.31.514538; doi: https://doi.org/10.1101/2022.10.31.514538

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4237)
  • Biochemistry (9159)
  • Bioengineering (6797)
  • Bioinformatics (24054)
  • Biophysics (12149)
  • Cancer Biology (9564)
  • Cell Biology (13819)
  • Clinical Trials (138)
  • Developmental Biology (7654)
  • Ecology (11731)
  • Epidemiology (2066)
  • Evolutionary Biology (15536)
  • Genetics (10664)
  • Genomics (14352)
  • Immunology (9504)
  • Microbiology (22883)
  • Molecular Biology (9120)
  • Neuroscience (49092)
  • Paleontology (357)
  • Pathology (1487)
  • Pharmacology and Toxicology (2576)
  • Physiology (3851)
  • Plant Biology (8349)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2300)
  • Systems Biology (6204)
  • Zoology (1302)