Object Recognition: Do rats see like we see?

Like primates, the rat brain areas thought to be involved in visual object recognition are arranged in a hierarchy.
  1. Nicole C Rust  Is a corresponding author
  1. University of Pennsylvania, United States

In our eyes, cells called photoreceptors convert the world around us into a pixel-like representation. Our brains must then reorganize this into a representation that reflects the identities of the objects we are looking at. The same object can be represented by very different pixel patterns, depending on its distance from us, the viewing angle and the lighting conditions. Conversely, different objects can be represented by pixel patterns that are similar. This is what makes object recognition a tremendously challenging problem for our brains to solve, and we do not fully understand how our brains manage to recognize objects.

Nonhuman primates (such as rhesus monkeys) are routinely used to study object recognition because their brains are similar to ours in many ways. However, there are advantages to working with mice and rats, including access to an array of modern biotechnological tools that have been optimized for these species. These tools include sophisticated ways to measure neural activity (Svoboda and Yasuda, 2006), to manipulate neural activity (Fenno et al., 2011), and to map how neurons are connected together within and between brain areas (Oh et al., 2014).

Skepticism that rodents could be used to gain insight into object recognition has largely been targeted at the ways in which rodent visual systems deviate from our own. For example, the retinae of mice and rats are specialized for seeing in the dark, and they lack a region called the fovea that allows humans to see objects in great detail at the center of the gaze. The visual cortex is also organized differently in primates and rodents with regard to how neurons with similar preferences for visual stimuli are clustered together within each brain area, and a much smaller fraction of the rodent cortex is devoted to visual processing. In light of all of these differences, can we really learn much about how our brains recognize objects by studying how rodents see?

In an earlier study, Davide Zoccolan and colleagues presented behavioral evidence that rats are capable of identifying objects under variations in viewing conditions (Zoccolan et al., 2009). Now, in eLife, Zoccolan and co-workers at SISSA in Trieste, the Istituto Italiano di Tecnologia and Harvard Medical School – including Sina Tafazoli and Houman Safaai as joint first authors – present evidence that this behavior is supported by four visual areas of the brain that are arranged in a functional hierarchy (Tafazoli et al., 2017). This is analogous to how object processing happens in the primate brain (DiCarlo et al., 2012).

Researchers had previously relied on anatomical evidence to argue that visual brain areas in rats are organized in a hierarchical fashion (Coogan and Burkhalter, 1993). Tafazoli et al. recorded the activity of four of these areas – termed V1, LM, LI and LL – in response to different objects as they systematically changed a number of variables (such as the position, size and luminance of each object). With this data, they quantified how much information each brain area reflected about the identity of the object, as well as how that information was formatted.

A key insight came from analyzing the degree to which changes in the neural responses to different objects could be attributed to differences in object luminance as opposed to object shape. Compared to the other brain areas, the firing rate of the neurons in V1 (the first brain area in the hierarchy) depended more strongly on the amount of luminance within the region of the visual field that each neuron was sensitive to. Moving through the hierarchy, an increasingly large proportion of the responses of the neurons reflected information about the shape of the object. At the same time, there was a systematic increase in the degree to which information about object identity was formatted in a manner that would make it easy for higher brain areas to access this information (DiCarlo and Cox, 2007).

In the face of considerable evidence that object processing in rats and primates is different, Tafazoli et al. have uncovered a compelling similarity. By design, their study has strong parallels with the studies that established a hierarchy for object processing in the primate brain, and their results suggest that rats and primates may perform object recognition in broadly similar ways. Future work will be required to determine the degree to which the nuts-and-bolts of object processing are in fact the same between the species.

References

    1. Coogan TA
    2. Burkhalter A
    (1993)
    Hierarchical organization of areas in rat visual cortex
    Journal of Neuroscience 13:3749–3772.

Article and author information

Author details

  1. Nicole C Rust

    Department of Psychology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    nrust@sas.upenn.edu
    Competing interests
    The author declares that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7820-6696

Publication history

  1. Version of Record published:

Copyright

© 2017, Rust

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,022
    views
  • 205
    downloads
  • 0
    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. Nicole C Rust
(2017)
Object Recognition: Do rats see like we see?
eLife 6:e26401.
https://doi.org/10.7554/eLife.26401

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Brian DePasquale, Carlos D Brody, Jonathan W Pillow
    Research Article Updated

    Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.

    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.