Contrast-polarity specific mapping improves efficiency of neuronal computation for collision detection

  1. Richard Burkett Dewell  Is a corresponding author
  2. Ying Zhu
  3. Margaret Eisenbrandt
  4. Richard Morse
  5. Fabrizio Gabbiani
  1. Baylor College of Medicine, United States
  2. Houston Methodist Research Institute, United States
  3. Rice University, United States

Abstract

Neurons receive information through their synaptic inputs, but the functional significance of how those inputs are mapped on to a cell's dendrites remains unclear. We studied this question in a grasshopper visual neuron that tracks approaching objects and triggers escape behavior before an impending collision. In response to black approaching objects, the neuron receives OFF excitatory inputs that form a retinotopic map of the visual field onto compartmentalized, distal dendrites. Subsequent processing of these OFF inputs by active membrane conductances allows the neuron to discriminate the spatial coherence of such stimuli. In contrast, we show that ON excitatory synaptic inputs activated by white approaching objects map in a random manner onto a more proximal dendritic field of the same neuron. The lack of retinotopic synaptic arrangement results in the neuron’s inability to discriminate the coherence of white approaching stimuli. Yet, the neuron retains the ability to discriminate stimulus coherence for checkered stimuli of mixed ON/OFF polarity. The coarser mapping and processing of ON stimuli thus has a minimal impact, while reducing the total energetic cost of the circuit. Further, we show that these differences in ON/OFF neuronal processing are behaviorally relevant, being tightly correlated with the animal’s escape behavior to light and dark stimuli of variable coherence. Our results show that the synaptic mapping of excitatory inputs affects the fine stimulus discrimination ability of single neurons and document the resulting functional impact on behavior.

Data availability

The data and code used to generate the final figures is available from Dryad (DOI: 10.5061/dryad.prr4xgxqp). The modeling code is available from ModelDB (Accession number: 267594).

The following data sets were generated

Article and author information

Author details

  1. Richard Burkett Dewell

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    For correspondence
    dewell@bcm.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2430-8184
  2. Ying Zhu

    Houston Methodist Research Institute, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Margaret Eisenbrandt

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0190-1922
  4. Richard Morse

    Rice University, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Fabrizio Gabbiani

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4966-3027

Funding

National Science Foundation (DMS-1120952)

  • Fabrizio Gabbiani

National Science Foundation (DBI-2021795)

  • Fabrizio Gabbiani

National Eye Institute (EY-002520-37)

  • Fabrizio Gabbiani

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Dewell 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

  • 604
    views
  • 74
    downloads
  • 6
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

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)

  1. Richard Burkett Dewell
  2. Ying Zhu
  3. Margaret Eisenbrandt
  4. Richard Morse
  5. Fabrizio Gabbiani
(2022)
Contrast-polarity specific mapping improves efficiency of neuronal computation for collision detection
eLife 11:e79772.
https://doi.org/10.7554/eLife.79772

Share this article

https://doi.org/10.7554/eLife.79772

Further reading

    1. Neuroscience
    Roshani Nhuchhen Pradhan, Craig Montell, Youngseok Lee
    Research Article

    The question as to whether animals taste cholesterol taste is not resolved. This study investigates whether the fruit fly, Drosophila melanogaster, is capable of detecting cholesterol through their gustatory system. We found that flies are indifferent to low levels of cholesterol and avoid higher levels. The avoidance is mediated by gustatory receptor neurons (GRNs), demonstrating that flies can taste cholesterol. The cholesterol-responsive GRNs comprise a subset that also responds to bitter substances. Cholesterol detection depends on five ionotropic receptor (IR) family members, and disrupting any of these genes impairs the flies' ability to avoid cholesterol. Ectopic expressions of these IRs in GRNs reveals two classes of cholesterol receptors, each with three shared IRs and one unique subunit. Additionally, expressing cholesterol receptors in sugar-responsive GRNs confers attraction to cholesterol. This study reveals that flies can taste cholesterol, and that the detection depends on IRs in GRNs.

    1. Neuroscience
    Kayson Fakhar, Fatemeh Hadaeghi ... Claus C Hilgetag
    Research Article

    Efficient communication in brain networks is foundational for cognitive function and behavior. However, how communication efficiency is defined depends on the assumed model of signaling dynamics, e.g., shortest path signaling, random walker navigation, broadcasting, and diffusive processes. Thus, a general and model-agnostic framework for characterizing optimal neural communication is needed. We address this challenge by assigning communication efficiency through a virtual multi-site lesioning regime combined with game theory, applied to large-scale models of human brain dynamics. Our framework quantifies the exact influence each node exerts over every other, generating optimal influence maps given the underlying model of neural dynamics. These descriptions reveal how communication patterns unfold if regions are set to maximize their influence over one another. Comparing these maps with a variety of brain communication models showed that optimal communication closely resembles a broadcasting regime in which regions leverage multiple parallel channels for information dissemination. Moreover, we found that the brain’s most influential regions are its rich-club, exploiting their topological vantage point by broadcasting across numerous pathways that enhance their reach even if the underlying connections are weak. Altogether, our work provides a rigorous and versatile framework for characterizing optimal brain communication, and uncovers the most influential brain regions, and the topological features underlying their influence.