Accuracy of Rats in Discriminating Visual Objects Is Explained by the Complexity of Their Perceptual Strategy

Neural correlates of visual object recognition in rats

Invariant object recognition-the ability to recognize objects across size, rotation, or context-is fundamental for making sense of a dynamic visual world. Though traditionally studied in primates, emerging evidence suggests rodents recognize objects across a range of identity-preserving transformations. We demonstrate that rats robustly perform visual object recognition and explore a neural pathway that may underlie this capacity by developing a pipeline from high-throughput behavior training to cellular resolution imaging in awake, head-fixed animals. Leveraging our optical approach, we systematically profile neurons in primary and higher-order visual areas and their spatial organization. We find that rat visual cortex exhibits several features similar to those observed in the primate ventral stream but also marked deviations, suggesting species-specific differences in how brains solve visual object recognition. This work reinforces the sophisticated visual abilities of rats and offers the technical foundation to use them as a powerful model for mechanistic perception.

Face processing in animal models: implications for autism spectrum disorder

Processing facial features is crucial to identify social partners (prey, predators, or conspecifics) and recognize and accurately interpret emotional expressions. Numerous studies in both human and non-human primates provided evidence promoting the notion of inherent mechanisms for detecting facial features. These mechanisms support a representation of faces independent of prior experiences and are vital for subsequent development in social and language domains. Moreover, deficits in processing faces are a reliable biomarker of autism spectrum disorder, appearing early and correlating with symptom severity. Face processing, however, is not only a prerogative of humans: other species also show remarkable face detection abilities. In this review, we present an overview of the current literature on face detection in vertebrate models that could be relevant to the study of autism.

Rodents' visual gamma as a biomarker of pathological neural conditions

Neural gamma oscillations (indicatively 30-100 Hz) are ubiquitous: they are associated with a broad range of functions in multiple cortical areas and across many animal species. Experimental and computational works established gamma rhythms as a global emergent property of neuronal networks generated by the balanced and coordinated interaction of excitation and inhibition. Coherently, gamma activity is strongly influenced by the alterations of synaptic dynamics which are often associated with pathological neural dysfunctions. We argue therefore that these oscillations are an optimal biomarker for probing the mechanism of cortical dysfunctions. Gamma oscillations are also highly sensitive to external stimuli in sensory cortices, especially the primary visual cortex (V1), where the stimulus dependence of gamma oscillations has been thoroughly investigated. Gamma manipulation by visual stimuli tuning is particularly easy in rodents, which have become a standard animal model for investigating the effects of network alterations on gamma oscillations. Overall, gamma in the rodents' visual cortex offers an accessible probe on dysfunctional information processing in pathological conditions. Beyond vision-related dysfunctions, alterations of gamma oscillations in rodents were indeed also reported in neural deficits such as migraine, epilepsy and neurodegenerative or neuropsychiatric conditions such as Alzheimer's, schizophrenia and autism spectrum disorders. Altogether, the connections between visual cortical gamma activity and physio-pathological conditions in rodent models underscore the potential of gamma oscillations as markers of neuronal (dys)functioning.

A computationally informed comparison between the strategies of rodents and humans in visual object recognition

Many species are able to recognize objects, but it has been proven difficult to pinpoint and compare how different species solve this task. Recent research suggested to combine computational and animal modelling in order to obtain a more systematic understanding of task complexity and compare strategies between species. In this study, we created a large multidimensional stimulus set and designed a visual discrimination task partially based upon modelling with a convolutional deep neural network (CNN). Experiments included rats (N = 11; 1115 daily sessions in total for all rats together) and humans (N = 45). Each species was able to master the task and generalize to a variety of new images. Nevertheless, rats and humans showed very little convergence in terms of which object pairs were associated with high and low performance, suggesting the use of different strategies. There was an interaction between species and whether stimulus pairs favoured early or late processing in a CNN. A direct comparison with CNN representations and visual feature analyses revealed that rat performance was best captured by late convolutional layers and partially by visual features such as brightness and pixel-level similarity, while human performance related more to the higher-up fully connected layers. These findings highlight the additional value of using a computational approach for the design of object recognition tasks. Overall, this computationally informed investigation of object recognition behaviour reveals a strong discrepancy in strategies between rodent and human vision.

The importance of contrast features in rat vision

Models of object recognition have mostly focused upon the hierarchical processing of objects from local edges up to more complex shape features. An alternative strategy that might be involved in pattern recognition centres around coarse-level contrast features. In humans and monkeys, the use of such features is most documented in the domain of face perception. Given prior suggestions that, generally, rodents might rely upon contrast features for object recognition, we hypothesized that they would pick up the typical contrast features relevant for face detection. We trained rats in a face-nonface categorization task with stimuli previously used in computer vision and tested for generalization with new, unseen stimuli by including manipulations of the presence and strength of a range of contrast features previously identified to be relevant for face detection. Although overall generalization performance was low, it was significantly modulated by contrast features. A model taking into account the summed strength of contrast features predicted the variation in accuracy across stimuli. Finally, with deep neural networks, we further investigated and quantified the performance and representations of the animals. The findings suggest that rat behaviour in visual pattern recognition tasks is partially explained by contrast feature processing.

Efficient processing of natural scenes in visual cortex

Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.

Mice recognize 3D objects from recalled 2D pictures, support for picture-object equivalence

Picture-object equivalence or recognizing a three-dimensional (3D) object after viewing a two-dimensional (2D) photograph of that object, is a higher-order form of visual cognition that may be beyond the perceptual ability of rodents. Behavioral and neurobiological mechanisms supporting picture-object equivalence are not well understood. We used a modified visual recognition memory task, reminiscent of those used for primates, to test whether picture-object equivalence extends to mice. Mice explored photographs of an object during a sample session, and 24 h later were presented with the actual 3D object from the photograph and a novel 3D object, or the stimuli were once again presented in 2D form. Mice preferentially explored the novel stimulus, indicating recognition of the “familiar” stimulus, regardless of whether the sample photographs depicted radially symmetric or asymmetric, similar, rotated, or abstract objects. Discrimination did not appear to be guided by individual object features or low-level visual stimuli. Inhibition of CA1 neuronal activity in dorsal hippocampus impaired discrimination, reflecting impaired memory of the 2D sample object. Collectively, results from a series of experiments provide strong evidence that picture-object equivalence extends to mice and is hippocampus-dependent, offering important support for the appropriateness of mice for investigating mechanisms of human cognition.

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Previously, in Hermundstad et al., 2014, we showed that local multipoint correlation patterns that are most variable in natural images are also the most perceptually salient for human observers, in a way that is compatible with the efficient coding principle. Understanding the neuronal mechanisms underlying such adaptation to image statistics will require performing invasive experiments that are impossible in humans. Therefore, it is important to understand whether a similar phenomenon can be detected in animal species that allow for powerful experimental manipulations, such as rodents. Here we selected four image statistics (from single- to four-point correlations) and trained four groups of rats to discriminate between white noise patterns and binary textures containing variable intensity levels of one of such statistics. We interpreted the resulting psychometric data with an ideal observer model, finding a sharp decrease in sensitivity from two- to four-point correlations and a further decrease from four- to three-point. This ranking fully reproduces the trend we previously observed in humans, thus extending a direct demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

Neurons of rat motor cortex become active during both grasping execution and grasping observation

In non-human primates, a subset of frontoparietal neurons (mirror neurons) respond both when an individual executes an action and when it observes another individual performing a similar action.^1-8 Mirror neurons constitute an observation and execution matching system likely involved in others' actions processing^3,5,9 and in a large set of complex cognitive functions.^10,11 Here, we show that the forelimb motor cortex of rats contains neurons presenting mirror properties analogous to those observed in macaques. We provide this evidence by event-related potentials acquired by microelectrocorticography and intracortical single-neuron activity, recorded from the same cortical region during grasping execution and observation. Mirror responses are highly specific, because grasping-related neurons do not respond to the observation of either grooming actions or graspable food alone. These results demonstrate that mirror neurons are present already in species phylogenetically distant from primates, suggesting for them a fundamental, albeit basic, role not necessarily related to higher cognitive functions. Moreover, because murine models have long been valued for their superior experimental accessibility and rapid life cycle, the present finding opens an avenue to new empirical studies tackling questions such as the innate or acquired origin of sensorimotor representations and the effects of social and environmental deprivation on sensorimotor development and recovery.

Rats spontaneously perceive global motion direction of drifting plaids

Computing global motion direction of extended visual objects is a hallmark of primate high-level vision. Although neurons selective for global motion have also been found in mouse visual cortex, it remains unknown whether rodents can combine multiple motion signals into global, integrated percepts. To address this question, we trained two groups of rats to discriminate either gratings (G group) or plaids (i.e., superpositions of gratings with different orientations; P group) drifting horizontally along opposite directions. After the animals learned the task, we applied a visual priming paradigm, where presentation of the target stimulus was preceded by the brief presentation of either a grating or a plaid. The extent to which rat responses to the targets were biased by such prime stimuli provided a measure of the spontaneous, perceived similarity between primes and targets. We found that gratings and plaids, when used as primes, were equally effective at biasing the perception of plaid direction for the rats of the P group. Conversely, for the G group, only the gratings acted as effective prime stimuli, while the plaids failed to alter the perception of grating direction. To interpret these observations, we simulated a decision neuron reading out the representations of gratings and plaids, as conveyed by populations of either component or pattern cells (i.e., local or global motion detectors). We concluded that the findings for the P group are highly consistent with the existence of a population of pattern cells, playing a functional role similar to that demonstrated in primates. We also explored different scenarios that could explain the failure of the plaid stimuli to elicit a sizable priming magnitude for the G group. These simulations yielded testable predictions about the properties of motion representations in rodent visual cortex at the single-cell and circuitry level, thus paving the way to future neurophysiology experiments.

An Open Source Platform for Presenting Dynamic Visual Stimuli

Operant behavior procedures often rely on visual stimuli to cue the initiation or secession of a response, and to provide a means for discriminating between two or more simultaneously available responses. While primate and human studies typically use Liquid-Crystal Display (LCD) or Organic Light-Emitting Diode (OLED) monitors and touch screens, rodent studies use a variety of methods to present visual cues ranging from traditional incandescent light bulbs, single LEDs, and, more recently, touch screen monitors. Commercially available systems for visual stimulus presentation are costly, challenging to customize, and are typically closed source. We developed an open-source, highly-modifiable visual stimulus presentation platform that can be combined with a 3D-printed operant response device. The device uses an 8 × 8 matrix of LEDs, and can be expanded to control much larger LED matrices. Implementing the platform is low-cost (<$70 USD per device in the year 2020). Using the platform, we trained rats to make nosepoke responses and discriminate between two distinct visual cues in a location-independent manner. This visual stimulus presentation platform is a cost-effective way to implement complex visually-guided operant behavior, including the use of moving or dynamically changing visual stimuli.

A temporal hierarchical feedforward model explains both the time and the accuracy of object recognition

Brain can recognize different objects as ones it has previously experienced. The recognition accuracy and its processing time depend on different stimulus properties such as the viewing conditions, the noise levels, etc. Recognition accuracy can be explained well by different models. However, most models paid no attention to the processing time, and the ones which do, are not biologically plausible. By modifying a hierarchical spiking neural network (spiking HMAX), the input stimulus is represented temporally within the spike trains. Then, by coupling the modified spiking HMAX model, with an accumulation-to-bound decision-making model, the generated spikes are accumulated over time. The input category is determined as soon as the firing rates of accumulators reaches a threshold (decision bound). The proposed object recognition model accounts for both recognition time and accuracy. Results show that not only does the model follow human accuracy in a psychophysical task better than the well-known non-temporal models, but also it predicts human response time in each choice. Results provide enough evidence that the temporal representation of features is informative, since it can improve the accuracy of a biologically plausible decision maker over time. In addition, the decision bound is able to adjust the speed-accuracy trade-off in different object recognition tasks.

Using deep neural networks to evaluate object vision tasks in rats

In the last two decades rodents have been on the rise as a dominant model for visual neuroscience. This is particularly true for earlier levels of information processing, but a number of studies have suggested that also higher levels of processing such as invariant object recognition occur in rodents. Here we provide a quantitative and comprehensive assessment of this claim by comparing a wide range of rodent behavioral and neural data with convolutional deep neural networks. These networks have been shown to capture hallmark properties of information processing in primates through a succession of convolutional and fully connected layers. We find that performance on rodent object vision tasks can be captured using low to mid-level convolutional layers only, without any convincing evidence for the need of higher layers known to simulate complex object recognition in primates. Our approach also reveals surprising insights on assumptions made before, for example, that the best performing animals would be the ones using the most abstract representations-which we show to likely be incorrect. Our findings suggest a road ahead for further studies aiming at quantifying and establishing the richness of representations underlying information processing in animal models at large.

Transient Disruption of the Inferior Parietal Lobule Impairs the Ability to Attribute Intention to Action

Although it is well established that fronto-parietal regions are active during action observation, whether they play a causal role in the ability to infer others' intentions from visual kinematics remains undetermined. In the experiments reported here, we combined offline continuous theta burst stimulation (cTBS) with computational modeling to reveal and causally probe single-trial computations in the inferior parietal lobule (IPL) and inferior frontal gyrus (IFG). Participants received cTBS over the left anterior IPL and the left IFG pars orbitalis in separate sessions before completing an intention discrimination task (discriminate intention of observed reach-to-grasp acts) or a kinematic discrimination task unrelated to intention (discriminate peak wrist height of the same acts). We targeted intention-sensitive regions whose fMRI activity, recorded when observing the same reach-to-grasp acts, could accurately discriminate intention. We found that transient disruption of activity of the left IPL, but not the IFG, impaired the observer's ability to attribute intention to action. Kinematic discrimination unrelated to intention, in contrast, was largely unaffected. Computational analyses of how encoding (mapping of intention to movement kinematics) and readout (mapping of kinematics to intention choices) intersect at the single-trial level revealed that IPL cTBS did not diminish the overall sensitivity of intention readout to movement kinematics. Rather, it selectively misaligned intention readout with respect to encoding, deteriorating mapping from informative kinematic features to intention choices. These results provide causal evidence of how the left anterior IPL computes mapping from kinematics to intentions.

Ventral hippocampus interacts with prelimbic cortex during inhibition of threat response via learned safety in both mice and humans

Heightened fear and inefficient safety learning are key features of fear and anxiety disorders. Evidence-based interventions for anxiety disorders, such as cognitive behavioral therapy, primarily rely on mechanisms of fear extinction. However, up to 50% of clinically anxious individuals do not respond to current evidence-based treatment, suggesting a critical need for new interventions based on alternative neurobiological pathways. Using parallel human and rodent conditioned inhibition paradigms alongside brain imaging methodologies, we investigated neural activity patterns in the ventral hippocampus in response to stimuli predictive of threat or safety and compound cues to test inhibition via safety in the presence of threat. Distinct hippocampal responses to threat, safety, and compound cues suggest that the ventral hippocampus is involved in conditioned inhibition in both mice and humans. Moreover, unique response patterns within target-differentiated subpopulations of ventral hippocampal neurons identify a circuit by which fear may be inhibited via safety. Specifically, ventral hippocampal neurons projecting to the prelimbic cortex, but not to the infralimbic cortex or basolateral amygdala, were more active to safety and compound cues than threat cues, and activity correlated with freezing behavior in rodents. A corresponding distinction was observed in humans: hippocampal-dorsal anterior cingulate cortex functional connectivity-but not hippocampal-anterior ventromedial prefrontal cortex or hippocampal-basolateral amygdala connectivity-differentiated between threat, safety, and compound conditions. These findings highlight the potential to enhance treatment for anxiety disorders by targeting an alternative neural mechanism through safety signal learning.

Face categorization and behavioral templates in rats

Rodents have become a popular model in vision science. It is still unclear how vision in rodents relates to primate vision when it comes to complex visual tasks. Here we report on the results of training rats in a face-categorization and generalization task. Additionally, the Bubbles paradigm is used to determine the behavioral templates of the animals. We found that rats are capable of face categorization and can generalize to previously unseen exemplars. Performance is affected-but remains above chance-by stimulus modifications such as upside-down and contrast-inverted stimuli. The behavioral templates of the rats overlap with a pixel-based template, with a bias toward the upper left parts of the stimuli. Together, these findings significantly expand the evidence about the extent to which rats learn complex visual-categorization tasks.

A passive, camera-based head-tracking system for real-time, three-dimensional estimation of head position and orientation in rodents

Tracking head position and orientation in small mammals is crucial for many applications in the field of behavioral neurophysiology, from the study of spatial navigation to the investigation of active sensing and perceptual representations. Many approaches to head tracking exist, but most of them only estimate the 2D coordinates of the head over the plane where the animal navigates. Full reconstruction of the pose of the head in 3D is much more more challenging and has been achieved only in handful of studies, which employed headsets made of multiple LEDs or inertial units. However, these assemblies are rather bulky and need to be powered to operate, which prevents their application in wireless experiments and in the small enclosures often used in perceptual studies. Here we propose an alternative approach, based on passively imaging a lightweight, compact, 3D structure, painted with a pattern of black dots over a white background. By applying a cascade of feature extraction algorithms that progressively refine the detection of the dots and reconstruct their geometry, we developed a tracking method that is highly precise and accurate, as assessed through a battery of validation measurements. We show that this method can be used to study how a rat samples sensory stimuli during a perceptual discrimination task and how a hippocampal place cell represents head position over extremely small spatial scales. Given its minimal encumbrance and wireless nature, our method could be ideal for high-throughput applications, where tens of animals need to be simultaneously and continuously tracked.NEW & NOTEWORTHY Head tracking is crucial in many behavioral neurophysiology studies. Yet reconstruction of the head's pose in 3D is challenging and typically requires implanting bulky, electrically powered headsets that prevent wireless experiments and are hard to employ in operant boxes. Here we propose an alternative approach, based on passively imaging a compact, 3D dot pattern that, once implanted over the head of a rodent, allows estimating the pose of its head with high precision and accuracy.

Precise relative-quantity judgement in the striped field mouse Apodemus agrarius Pallas

Applying the classical experimental scheme of training animals with food rewards to discriminate between quantities of visual stimuli, we demonstrated that not only can striped field mice Apodemus agrarius discriminate between clearly distinctive quantities such as 5 and 10, but some of these mice also exhibit high accuracy in discriminating between quantities that differ only by one. The latter include both small (such as 2 versus 3) and relatively large (such as 5 versus 6, and 8 versus 9) quantities of elements. This is the first evidence of precise relative-quantity judgement in wild rodents. We found striking individual variation in cognitive performance among striped field mice, which possibly reflects individual cognitive variation in natural populations. We speculate that high accuracy in differentiating large quantities is based on the adaptive ability of wild rodents to capture subtle changes in their environment. We suggest that the striped field mouse may be a powerful model species to develop advanced cognitive tests for comparative studies of numerical competence in animals and for understanding evolutionary roots of quantity processing.

Nonlinear Processing of Shape Information in Rat Lateral Extrastriate Cortex

In rodents, the progression of extrastriate areas located laterally to primary visual cortex (V1) has been assigned to a putative object-processing pathway (homologous to the primate ventral stream), based on anatomical considerations. Recently, we found functional support for such attribution (Tafazoli et al., 2017), by showing that this cortical progression is specialized for coding object identity despite view changes, the hallmark property of a ventral-like pathway. Here, we sought to clarify what computations are at the base of such specialization. To this aim, we performed multielectrode recordings from V1 and laterolateral area LL (at the apex of the putative ventral-like hierarchy) of male adult rats, during the presentation of drifting gratings and noise movies. We found that the extent to which neuronal responses were entrained to the phase of the gratings sharply dropped from V1 to LL, along with the quality of the receptive fields inferred through reverse correlation. Concomitantly, the tendency of neurons to respond to different oriented gratings increased, whereas the sharpness of orientation tuning declined. Critically, these trends are consistent with the nonlinear summation of visual inputs that is expected to take place along the ventral stream, according to the predictions of hierarchical models of ventral computations and a meta-analysis of the monkey literature. This suggests an intriguing homology between the mechanisms responsible for building up shape selectivity and transformation tolerance in the visual cortex of primates and rodents, reasserting the potential of the latter as models to investigate ventral stream functions at the circuitry level.SIGNIFICANCE STATEMENT Despite the growing popularity of rodents as models of visual functions, it remains unclear whether their visual cortex contains specialized modules for processing shape information. To addresses this question, we compared how neuronal tuning evolves from rat primary visual cortex (V1) to a downstream visual cortical region (area LL) that previous work has implicated in shape processing. In our experiments, LL neurons displayed a stronger tendency to respond to drifting gratings with different orientations while maintaining a sustained response across the whole duration of the drift cycle. These trends match the increased complexity of pattern selectivity and the augmented tolerance to stimulus translation found in monkey visual temporal cortex, thus revealing a homology between shape processing in rodents and primates.

Human-like perceptual masking is difficult to observe in rats performing an orientation discrimination task

Visual masking occurs when the perception of a brief target stimulus is affected by a preceding or succeeding mask. The uncoupling of the target and its perception allows an opportunity to investigate the neuronal mechanisms involved in sensory representation and visual perception. To determine whether rats are a suitable model for subsequent studies of the neuronal basis of visual masking, we first demonstrated that decoding of neuronal responses recorded in the primary visual cortex (V1) of anaesthetized rats predicted that orientation discrimination performance should decline when masking stimuli are presented immediately before or after oriented target stimuli. We then trained Long-Evans rats (n = 7) to discriminate between horizontal and vertical target Gabors or gratings. In some trials, a plaid mask was presented at varying stimulus onset asynchronies (SOAs) relative to the target. Spatially, the masks were presented either overlapping or surrounding the target location. In the absence of a mask, all animals could reliably discriminate orientation when stimulus durations were 16 ms or longer. In the presence of a mask, discrimination performance was impaired, but did not systematically vary with SOA as is typical of visual masking. In humans performing a similar task, we found visual masking impaired perception of the target at short SOAs regardless of the spatial or temporal configuration of stimuli. Our findings indicate that visual masking may be difficult to observe in rats as the stimulus parameters necessary to quantify masking will make the task so difficult that it prevents robust measurement of psychophysical performance. Thus, our results suggest that rats may not be an ideal model to investigate the effects of visual masking on perception.

Object Recognition: Complexity of Recognition Strategies

Primate brains and state-of-the-art convolutional neural networks can recognize many faces, objects and scenes, though how they do so is often mysterious. New research unveils some of the mystery, revealing unexpected complexity in the recognition strategies of rodents.