Visual Categorization of Natural Movies by Rats

Vinken, Kasper; Vermaercke, Ben; Beeck, Hans Op de

doi:10.1523/jneurosci.3663-13.2014

Cited by 40 publications

(74 citation statements)

References 50 publications

(13 reference statements)

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“…We focus on three landmark studies: the first showed that rats can learn to discriminate two objects invariant to changes in size and azimuth-rotation (Zoccolan et al, 2009), the second found that rats employ different object recognition strategies that seem to vary in complexity (Djurdjevic et al, 2018), the third showed that rats are capable of ordinate-level categorization of natural videos (Vinken et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…Up to this point, we have only discussed studies that used a small number of computer-graphics renderings of abstract and more naturalistic objects. However, rats have also been shown to be able to learn category rules from more complex natural videos that generalize to novel category exemplars (Vinken et al, 2014). In this study, rats were trained in a visual water maze (Prusky et al, 2000) to classify videos featuring a rat (target category), from phase scrambled versions of the target videos and target-matched natural distractor videos featuring various moving objects.…”

Section: Resultsmentioning

confidence: 99%

“…Each rat was trained a subset of 15 videos (purple), and tested for generalization with 40 novel videos (green). Ten test videos (the natural videos in the first green rectangle) were modified to further probe the rats, for example by reducing playback speed to 25% or equalizing average pixel values in the lower-half of the videos (see Vinken et al, 2014). c-e , average percentage correct classification of rat versus non-rat frame bin pairs by the classifier operating on the outputs of each layer in AlexNet, VGG16, and VGG11-C3D and for natural and scrambled distractors separately.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies have subsequently expanded on this work using similar stimuli (Tafazoli et al, 2012; Alemi-Neissi et al, 2013; Rosselli et al, 2015), and recently it was found that the success of rats in these tasks depends on the complexity of their strategy (Djurdjevic et al, 2018). In a study using natural stimuli, rats could generalize learned category rules to new, unseen category exemplars that differ from trained exemplars in complex ways (Vinken et al, 2014). Neurophysiological recordings have revealed neural responses in lateral visual areas that might underlie these behavioral abilities (Vermaercke et al, 2014; Tafazoli et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision

Vinken

Beeck

2020

Preprint

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1In the last two decades rodents have been on the rise as a dominant model for visual 2 neuroscience. This is particularly true for earlier levels of information processing, but 3 high-profile papers have suggested that also higher levels of processing such as invariant 4 object recognition occur in rodents. Here we provide a quantitative and comprehensive 5 assessment of this claim by comparing a wide range of rodent behavioral and neural data 6 with convolutional deep neural networks. These networks have been shown to capture 7 the richness of information processing in primates through a succession of convolutional 8 and fully connected layers. We find that rodent object vision can be captured using 9 low to mid-level convolutional layers only, without any convincing evidence for the 10 need of higher layers known to simulate complex object recognition in primates. Our 11 approach also reveals surprising insights on assumptions made before, for example, that 12 the best performing animals would be the ones using the most complex representations 13 -which we show to likely be incorrect. Our findings suggest a road ahead for further 14 studies aiming at quantifying and establishing the richness of representations underlying 15 information processing in animal models at large. 16 1 Introduction 17 Up to one decade ago, macaque monkeys were uncontested as the primary animal model for 18 vision research targeting information processing at the cortical level. Since then, studies on 19 rodents have become increasingly popular, in particular because developments in neurotech-20 nology such as cell-level imaging and optogenetics capitalized upon the existence of genetic 21 models. A major question emerging from this evolution is how far we can take rodents as a 22 model for the more complex aspects of visual information processing. 23To answer this question, a series of high-profile studies have documented the ability of rats 24 to perform seemingly complex object recognition tasks. In these tasks, rats show the ability 25 to recognize objects despite various transformations in viewing conditions such as position, 26 size, and viewpoint. In the first landmark study, Zoccolan and colleagues (2009) trained rats 27 2 to discriminate two computer-graphics rendered objects under various changes in size and 28 rotation, and showed that the animals could successfully generalize to novel combinations 29 of these transformations. Several studies have subsequently expanded on this work using 30 similar stimuli (Tafazoli et al., 2012; Alemi-Neissi et al., 2013; Rosselli et al., 2015), and 31 recently it was found that the success of rats in these tasks depends on the complexity of 32 their strategy (Djurdjevic et al., 2018). In a study using natural stimuli, rats could generalize 33 learned category rules to new, unseen category exemplars that differ from trained exemplars 34 in complex ways (Vinken et al., 2014). Neurophysiological recordings have revealed neural 35 responses in lateral visual areas that might underlie these b...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision

Vinken

Beeck

2020

Preprint

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“…Finally, it cannot be excluded the possibility that major differences exist, between mouse and rat visual cortex, in terms of high-order processing of shape information. In fact, despite some recent efforts (Aoki et al, 2017;Yu et al, 2018), mouse visual perception is still largely unexplored, and it remains unknown whether mice are able to perform those perceptual tasks, recently demonstrated in rats (Zoccolan et al, 2009;Tafazoli et al, 2012;Alemi-Neissi et al, 2013;Vinken et al, 2014;Rosselli et al, 2015;De Keyser et al, 2015;Djurdjevic et al, 2018), that should specifically engage the ventral stream.…”

Section: #### Figure 11 Near Here ####mentioning

confidence: 99%

Nonlinear processing of shape information in rat lateral extrastriate cortex

Matteucci

Marotti

Rosselli

et al. 2018

Preprint

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Dorsal hippocampus is necessary for visual categorization in rats

et al. 2018

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The hippocampus may play a role in categorization because of the need to differentiate stimulus categories (pattern separation) and to recognize category membership of stimuli from partial information (pattern completion). We hypothesized that the hippocampus would be more crucial for categorization of low-density (few relevant features) stimuli-due to the higher demand on pattern separation and pattern completion-than for categorization of high-density (many relevant features) stimuli. Using a touchscreen apparatus, rats were trained to categorize multiple abstract stimuli into two different categories. Each stimulus was a pentagonal configuration of five visual features; some of the visual features were relevant for defining the category whereas others were irrelevant. Two groups of rats were trained with either a high (dense, n = 8) or low (sparse, n = 8) number of category-relevant features. Upon reaching criterion discrimination (≥75% correct, on 2 consecutive days), bilateral cannulas were implanted in the dorsal hippocampus. The rats were then given either vehicle or muscimol infusions into the hippocampus just prior to various testing sessions. They were tested with: the previously trained stimuli (trained), novel stimuli involving new irrelevant features (novel), stimuli involving relocated features (relocation), and a single relevant feature (singleton). In training, the dense group reached criterion faster than the sparse group, indicating that the sparse task was more difficult than the dense task. In testing, accuracy of both groups was equally high for trained and novel stimuli. However, both groups showed impaired accuracy in the relocation and singleton conditions, with a greater deficit in the sparse group. The testing data indicate that rats encode both the relevant features and the spatial locations of the features. Hippocampal inactivation impaired visual categorization regardless of the density of the category-relevant features for the trained, novel, relocation, and singleton stimuli. Hippocampus-mediated pattern completion and pattern separation mechanisms may be necessary for visual categorization involving overlapping irrelevant features.

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Visual Categorization of Natural Movies by Rats

Cited by 40 publications

References 50 publications

Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision

Deep Neural Networks Point to Mid-level Complexity of Rodent Object Vision

Nonlinear processing of shape information in rat lateral extrastriate cortex

Dorsal hippocampus is necessary for visual categorization in rats

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