Temporal multiplexing of perception and memory codes in IT cortex.

A central assumption of neuroscience is that long-term memories are represented by the same brain areas that encode sensory stimuli1. Neurons in inferotemporal (IT) cortex represent the sensory percept of visual objects using a distributed axis code2-4. Whether and how the same IT neural population represents the long-term memory of visual objects remains unclear. Here we examined how familiar faces are encoded in the IT anterior medial face patch (AM), perirhinal face patch (PR) and temporal pole face patch (TP). In AM and PR we observed that the encoding axis for familiar faces is rotated relative to that for unfamiliar faces at long latency; in TP this memory-related rotation was much weaker. Contrary to previous claims, the relative response magnitude to familiar versus unfamiliar faces was not a stable indicator of familiarity in any patch5-11. The mechanism underlying the memory-related axis change is likely intrinsic to IT cortex, because inactivation of PR did not affect axis change dynamics in AM. Overall, our results suggest that memories of familiar faces are represented in AM and perirhinal cortex by a distinct long-latency code, explaining how the same cell population can encode both the percept and memory of faces.

How does the primate brain combine generative and discriminative computations in vision?

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of "inference" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes that give rise to it. In this conception, vision inverts a generative model through an interrogation of the sensory evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.

Effects of focused ultrasound in a "clean" mouse model of ultrasonic neuromodulation.

Recent studies on ultrasonic neuromodulation (UNM) in rodents have shown that focused ultrasound (FUS) can activate peripheral auditory pathways, leading to off-target and brain-wide excitation, which obscures the direct activation of the target area by FUS. To address this issue, we developed a new mouse model, the double transgenic Pou4f3+/DTR × Thy1-GCaMP6s, which allows for inducible deafening using diphtheria toxin and minimizes off-target effects of UNM while allowing effects on neural activity to be visualized with fluorescent calcium imaging. Using this model, we found that the auditory confounds caused by FUS can be significantly reduced or eliminated within a certain pressure range. At higher pressures, FUS can result in focal fluorescence dips at the target, elicit non-auditory sensory confounds, and damage tissue, leading to spreading depolarization. Under the acoustic conditions we tested, we did not observe direct calcium responses in the mouse cortex. Our findings provide a cleaner animal model for UNM and sonogenetics research, establish a parameter range within which off-target effects are confidently avoided, and reveal the non-auditory side effects of higher-pressure stimulation.

Rapid, concerted switching of the neural code in inferotemporal cortex.

A fundamental paradigm in neuroscience is the concept of neural coding through tuning functions 1 . According to this idea, neurons encode stimuli through fixed mappings of stimulus features to firing rates. Here, we report that the tuning of visual neurons can rapidly and coherently change across a population to attend to a whole and its parts. We set out to investigate a longstanding debate concerning whether inferotemporal (IT) cortex uses a specialized code for representing specific types of objects or whether it uses a general code that applies to any object. We found that face cells in macaque IT cortex initially adopted a general code optimized for face detection. But following a rapid, concerted population event lasting < 20 ms, the neural code transformed into a face-specific one with two striking properties: (i) response gradients to principal detection-related dimensions reversed direction, and (ii) new tuning developed to multiple higher feature space dimensions supporting fine face discrimination. These dynamics were face specific and did not occur in response to objects. Overall, these results show that, for faces, face cells shift from detection to discrimination by switching from an object-general code to a face-specific code. More broadly, our results suggest a novel mechanism for neural representation: concerted, stimulus-dependent switching of the neural code used by a cortical area.

Microscale visualization of cellular features in adult macaque visual cortex.

Expansion microscopy and light sheet imaging enable fine-scale resolution of intracellular features that comprise neural circuits. Most current techniques visualize sparsely distributed features across whole brains or densely distributed features within individual brain regions. Here, we visualize dense distributions of immunolabeled proteins across early visual cortical areas in adult macaque monkeys. This process may be combined with multiphoton or magnetic resonance imaging to produce multimodal atlases in large, gyrencephalic brains.

Functional modules for visual scene segmentation in macaque visual cortex.

Segmentation, the computation of object boundaries, is one of the most important steps in intermediate visual processing. Previous studies have reported cells across visual cortex that are modulated by segmentation features, but the functional role of these cells remains unclear. First, it is unclear whether these cells encode segmentation consistently since most studies used only a limited variety of stimulus types. Second, it is unclear whether these cells are organized into specialized modules or instead randomly scattered across the visual cortex: the former would lend credence to a functional role for putative segmentation cells. Here, we used fMRI-guided electrophysiology to systematically characterize the consistency and spatial organization of segmentation-encoding cells across the visual cortex. Using fMRI, we identified a set of patches in V2, V3, V3A, V4, and V4A that were more active for stimuli containing figures compared to ground, regardless of whether figures were defined by texture, motion, luminance, or disparity. We targeted these patches for single-unit recordings and found that cells inside segmentation patches were tuned to both figure-ground and borders more consistently across types of stimuli than cells in the visual cortex outside the patches. Remarkably, we found clusters of cells inside segmentation patches that showed the same border-ownership preference across all stimulus types. Finally, using a population decoding approach, we found that segmentation could be decoded with higher accuracy from segmentation patches than from either color-selective or control regions. Overall, our results suggest that segmentation signals are preferentially encoded in spatially discrete patches.

Effects of focused ultrasound in a "clean" mouse model of ultrasonic neuromodulation.

Recent studies on ultrasonic neuromodulation (UNM) in rodents have shown that focused ultrasound (FUS) can activate peripheral auditory pathways, leading to off-target and brain-wide excitation, which obscures the direct activation of the target area by FUS. To address this issue, we developed a new mouse model, the double transgenic Pou4f3+/DTR × Thy1-GCaMP6s, which allows for inducible deafening using diphtheria toxin and minimizes off-target effects of UNM while allowing effects on neural activity to be visualized with fluorescent calcium imaging. Using this model, we found that the auditory confounds caused by FUS can be significantly reduced or eliminated within a certain pressure range. At higher pressures, FUS can result in focal fluorescence dips at the target, elicit non-auditory sensory confounds, and damage tissue, leading to spreading depolarization. Under the acoustic conditions we tested, we did not observe direct calcium responses in the mouse cortex. Our findings provide a cleaner animal model for UNM and sonogenetics research, establish a parameter range within which off-target effects are confidently avoided, and reveal the non-auditory side effects of higher-pressure stimulation.

Large-scale high-density brain-wide neural recording in nonhuman primates.

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmatically select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.

Catalyzing next-generation Artificial Intelligence through NeuroAI.

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities - inherited from over 500 million years of evolution - that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

Mice and primates use distinct strategies for visual segmentation.

The rodent visual system has attracted great interest in recent years due to its experimental tractability, but the fundamental mechanisms used by the mouse to represent the visual world remain unclear. In the primate, researchers have argued from both behavioral and neural evidence that a key step in visual representation is 'figure-ground segmentation', the delineation of figures as distinct from backgrounds. To determine if mice also show behavioral and neural signatures of figure-ground segmentation, we trained mice on a figure-ground segmentation task where figures were defined by gratings and naturalistic textures moving counterphase to the background. Unlike primates, mice were severely limited in their ability to segment figure from ground using the opponent motion cue, with segmentation behavior strongly dependent on the specific carrier pattern. Remarkably, when mice were forced to localize naturalistic patterns defined by opponent motion, they adopted a strategy of brute force memorization of texture patterns. In contrast, primates, including humans, macaques, and mouse lemurs, could readily segment figures independent of carrier pattern using the opponent motion cue. Consistent with mouse behavior, neural responses to the same stimuli recorded in mouse visual areas V1, RL, and LM also did not support texture-invariant segmentation of figures using opponent motion. Modeling revealed that the texture dependence of both the mouse's behavior and neural responses could be explained by a feedforward neural network lacking explicit segmentation capabilities. These findings reveal a fundamental limitation in the ability of mice to segment visual objects compared to primates.

A topological solution to object segmentation and tracking.

The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: 1) segmenting visual input into discrete units and 2) tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question, Can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.

The application of artificial intelligence to biology and neuroscience.

Over the last decade, the artificial intelligence (AI) has undergone a revolution that is poised to transform the economy, society, and science. The pace of progress is staggering, and problems that seemed intractable just a few years ago have now been solved. The intersection between neuroscience and AI is particularly exciting.

Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons.

In order to better understand how the brain perceives faces, it is important to know what objective drives learning in the ventral visual stream. To answer this question, we model neural responses to faces in the macaque inferotemporal (IT) cortex with a deep self-supervised generative model, ß-VAE, which disentangles sensory data into interpretable latent factors, such as gender or age. Our results demonstrate a strong correspondence between the generative factors discovered by ß-VAE and those coded by single IT neurons, beyond that found for the baselines, including the handcrafted state-of-the-art model of face perception, the Active Appearance Model, and deep classifiers. Moreover, ß-VAE is able to reconstruct novel face images using signals from just a handful of cells. Together our results imply that optimising the disentangling objective leads to representations that closely resemble those in the IT at the single unit level. This points at disentangling as a plausible learning objective for the visual brain.

Explaining face representation in the primate brain using different computational models.

Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the "active appearance" model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch anterior medial (AM) to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification, such as illumination.

Combining brain perturbation and neuroimaging in non-human primates.

Brain perturbation studies allow detailed causal inferences of behavioral and neural processes. Because the combination of brain perturbation methods and neural measurement techniques is inherently challenging, research in humans has predominantly focused on non-invasive, indirect brain perturbations, or neurological lesion studies. Non-human primates have been indispensable as a neurobiological system that is highly similar to humans while simultaneously being more experimentally tractable, allowing visualization of the functional and structural impact of systematic brain perturbation. This review considers the state of the art in non-human primate brain perturbation with a focus on approaches that can be combined with neuroimaging. We consider both non-reversible (lesions) and reversible or temporary perturbations such as electrical, pharmacological, optical, optogenetic, chemogenetic, pathway-selective, and ultrasound based interference methods. Method-specific considerations from the research and development community are offered to facilitate research in this field and support further innovations. We conclude by identifying novel avenues for further research and innovation and by highlighting the clinical translational potential of the methods.

A new no-report paradigm reveals that face cells encode both consciously perceived and suppressed stimuli.

A powerful paradigm to identify neural correlates of consciousness is binocular rivalry, wherein a constant visual stimulus evokes a varying conscious percept. It has recently been suggested that activity modulations observed during rivalry may represent the act of report rather than the conscious percept itself. Here, we performed single-unit recordings from face patches in macaque inferotemporal (IT) cortex using a no-report paradigm in which the animal's conscious percept was inferred from eye movements. We found that large proportions of IT neurons represented the conscious percept even without active report. Furthermore, on single trials we could decode both the conscious percept and the suppressed stimulus. Together, these findings indicate that (1) IT cortex possesses a true neural correlate of consciousness and (2) this correlate consists of a population code wherein single cells multiplex representation of the conscious percept and veridical physical stimulus, rather than a subset of cells perfectly reflecting consciousness.

The macaque face patch system: a turtle's underbelly for the brain.

Objects constitute the fundamental currency of our consciousness: they are the things that we perceive, remember and think about. One of the most important objects for a primate is a face. Research on the macaque face patch system in recent years has given us a remarkable window into the detailed processes underlying object recognition. Here, we review the macaque face patch system, including its anatomical organization, coding principles, role in behaviour and interactions with other brain regions. We highlight not only how it constitutes an archetypal object recognition system but also how it may provide a key to understanding mechanisms for higher cognitive function.

The Mind of a Mouse.

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

A map of object space in primate inferotemporal cortex.

The inferotemporal (IT) cortex is responsible for object recognition, but it is unclear how the representation of visual objects is organized in this part of the brain. Areas that are selective for categories such as faces, bodies, and scenes have been found1-5, but large parts of IT cortex lack any known specialization, raising the question of what general principle governs IT organization. Here we used functional MRI, microstimulation, electrophysiology, and deep networks to investigate the organization of macaque IT cortex. We built a low-dimensional object space to describe general objects using a feedforward deep neural network trained on object classification6. Responses of IT cells to a large set of objects revealed that single IT cells project incoming objects onto specific axes of this space. Anatomically, cells were clustered into four networks according to the first two components of their preferred axes, forming a map of object space. This map was repeated across three hierarchical stages of increasing view invariance, and cells that comprised these maps collectively harboured sufficient coding capacity to approximately reconstruct objects. These results provide a unified picture of IT organization in which category-selective regions are part of a coarse map of object space whose dimensions can be extracted from a deep network.