Object Recognition: Do rats see like we see?
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
-
Hierarchical organization of areas in rat visual cortexJournal of Neuroscience 13:3749–3772.
-
Untangling invariant object recognitionTrends in Cognitive Sciences 11:333–341.https://doi.org/10.1016/j.tics.2007.06.010
-
The development and application of optogeneticsAnnual Review of Neuroscience 34:389–412.https://doi.org/10.1146/annurev-neuro-061010-113817
Article and author information
Author details
Publication history
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
-
- 2,006
- views
-
- 203
- downloads
-
- 0
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
Further reading
-
- Neuroscience
Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language’s word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker’s linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term ‘duration’ prior. These findings show that experience with a given language can shape a fundamental aspect of human perception – the neural processing of rhythmic sounds – and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.
-
- Cell Biology
- Neuroscience
Reactive astrocytes play critical roles in the occurrence of various neurological diseases such as multiple sclerosis. Activation of astrocytes is often accompanied by a glycolysis-dominant metabolic switch. However, the role and molecular mechanism of metabolic reprogramming in activation of astrocytes have not been clarified. Here, we found that PKM2, a rate-limiting enzyme of glycolysis, displayed nuclear translocation in astrocytes of EAE (experimental autoimmune encephalomyelitis) mice, an animal model of multiple sclerosis. Prevention of PKM2 nuclear import by DASA-58 significantly reduced the activation of mice primary astrocytes, which was observed by decreased proliferation, glycolysis and secretion of inflammatory cytokines. Most importantly, we identified the ubiquitination-mediated regulation of PKM2 nuclear import by ubiquitin ligase TRIM21. TRIM21 interacted with PKM2, promoted its nuclear translocation and stimulated its nuclear activity to phosphorylate STAT3, NF-κB and interact with c-myc. Further single-cell RNA sequencing and immunofluorescence staining demonstrated that TRIM21 expression was upregulated in astrocytes of EAE. TRIM21 overexpressing in mice primary astrocytes enhanced PKM2-dependent glycolysis and proliferation, which could be reversed by DASA-58. Moreover, intracerebroventricular injection of a lentiviral vector to knockdown TRIM21 in astrocytes or intraperitoneal injection of TEPP-46, which inhibit the nuclear translocation of PKM2, effectively decreased disease severity, CNS inflammation and demyelination in EAE. Collectively, our study provides novel insights into the pathological function of nuclear glycolytic enzyme PKM2 and ubiquitination-mediated regulatory mechanism that are involved in astrocyte activation. Targeting this axis may be a potential therapeutic strategy for the treatment of astrocyte-involved neurological disease.