Learning speed and detection sensitivity controlled by distinct cortico-fugal neurons in visual cortex

  1. Sarah Ruediger  Is a corresponding author
  2. Massimo Scanziani  Is a corresponding author
  1. UCSF and HHMI, United States

Abstract

Vertebrates can change their behavior upon detection of visual stimuli according to the outcome their actions produce. Such goal-directed behavior involves evolutionary conserved brain structures like the striatum and optic tectum, which receive ascending visual input from the periphery. In mammals, however, these structures also receive descending visual input from visual cortex (VC), via neurons that give rise to cortico-fugal projections. The function of cortico-fugal neurons in visually-guided, goal-directed behavior remains unclear. Here we address the impact of two populations of cortico-fugal neurons in mouse VC in the learning and performance of a visual detection task. We show that the ablation of striatal projecting neurons reduces learning speed while the ablation of superior colliculus projecting neurons does not impact learning but reduces detection sensitivity. This functional dissociation between distinct cortico-fugal neurons in controlling learning speed and detection sensitivity suggests an adaptive contribution of cortico-fugal pathways even in simple goal-directed behavior.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Numerical data for graphs represented in figures 1-6, figure 1-figure supplement 2,3,4,5, figure 2-figure supplement 1, figure 4-figure supplement 1 are provided as source data files. The software used to generate visual stimuli and record neuronal activity is available at: https://github.com/mscaudill/neuroGit and https://github.com/aresulaj/ResRueOlsSca18.

Article and author information

Author details

  1. Sarah Ruediger

    Physiology, UCSF and HHMI, San Francisco, United States
    For correspondence
    sarruedi@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Massimo Scanziani

    Physiology, UCSF and HHMI, San Francisco, United States
    For correspondence
    massimo@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5331-9686

Funding

National Eye Institute (NIH R01EY025668)

  • Massimo Scanziani

Howard Hughes Medical Institute

  • Massimo Scanziani

European Molecular Biology Organization (ALTF741-2012)

  • Sarah Ruediger

Swiss National Science Foundation (151168)

  • Sarah Ruediger

Swiss National Science Foundation (138719)

  • Sarah Ruediger

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 experimental procedures were performed with the approval of the Committee on Animal Care at UCSD and UCSF. Authorization # AN179056

Copyright

© 2020, Ruediger & Scanziani

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

  • 4,032
    views
  • 532
    downloads
  • 18
    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. Sarah Ruediger
  2. Massimo Scanziani
(2020)
Learning speed and detection sensitivity controlled by distinct cortico-fugal neurons in visual cortex
eLife 9:e59247.
https://doi.org/10.7554/eLife.59247

Share this article

https://doi.org/10.7554/eLife.59247

Further reading

    1. Neuroscience
    Paul I Jaffe, Gustavo X Santiago-Reyes ... Russell A Poldrack
    Research Article

    Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.

    1. Neuroscience
    Aneri Soni, Michael J Frank
    Research Article

    How and why is working memory (WM) capacity limited? Traditional cognitive accounts focus either on limitations on the number or items that can be stored (slots models), or loss of precision with increasing load (resource models). Here, we show that a neural network model of prefrontal cortex and basal ganglia can learn to reuse the same prefrontal populations to store multiple items, leading to resource-like constraints within a slot-like system, and inducing a trade-off between quantity and precision of information. Such ‘chunking’ strategies are adapted as a function of reinforcement learning and WM task demands, mimicking human performance and normative models. Moreover, adaptive performance requires a dynamic range of dopaminergic signals to adjust striatal gating policies, providing a new interpretation of WM difficulties in patient populations such as Parkinson’s disease, ADHD, and schizophrenia. These simulations also suggest a computational rather than anatomical limit to WM capacity.