A connectivity-constrained computational account of topographic organization in primate high-level visual cortex

Development of Higher-Level Vision: A Network Perspective

Most studies on the development of the visual system have focused on the mechanisms shaping early visual stages up to the level of primary visual cortex (V1). Much less is known about the development of the stages after V1 that handle the higher visual functions fundamental to everyday life. The standard model for the maturation of these areas is that it occurs sequentially, according to the positions of areas in the adult hierarchy. Yet, the existing literature reviewed here paints a different picture, one in which the adult configuration emerges through a sequence of unique network configurations that are not mere partial versions of the adult hierarchy. In addition to studying higher visual development per se to fill major gaps in knowledge, it will be crucial to adopt a network-level perspective in future investigations to unravel normal developmental mechanisms, identify vulnerabilities to developmental disorders, and eventually devise treatments for these disorders.

A Whole-Brain Topographic Ontology

It is a common view that the intricate array of specialized domains in the ventral visual pathway is innately prespecified. What this review postulates is that it is not. We explore the origins of domain specificity, hypothesizing that the adult brain emerges from an interplay between a domain-general map-based architecture, shaped by intrinsic mechanisms, and experience. We argue that the most fundamental innate organization of cortex in general, and not just the visual pathway, is a map-based topography that governs how the environment maps onto the brain, how brain areas interconnect, and ultimately, how the brain processes information.

Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes

Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us to map the natural image preferences of the neural population at 100-µm scale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4.

A unifying framework for functional organization in early and higher ventral visual cortex

A key feature of cortical systems is functional organization: the arrangement of functionally distinct neurons in characteristic spatial patterns. However, the principles underlying the emergence of functional organization in the cortex are poorly understood. Here, we develop the topographic deep artificial neural network (TDANN), the first model to predict several aspects of the functional organization of multiple cortical areas in the primate visual system. We analyze the factors driving the TDANN's success and find that it balances two objectives: learning a task-general sensory representation and maximizing the spatial smoothness of responses according to a metric that scales with cortical surface area. In turn, the representations learned by the TDANN are more brain-like than in spatially unconstrained models. Finally, we provide evidence that the TDANN's functional organization balances performance with between-area connection length. Our results offer a unified principle for understanding the functional organization of the primate ventral visual system.

Holistic processing and face expertise after pediatric resection of occipitotemporal cortex

The nature and extent of hemispheric lateralization and its potential for reorganization continues to be debated, although there is general agreement that there is a right hemisphere (RH) advantage for face processing in human adults. Here, we examined face processing and its lateralization in individuals with a single preserved occipitotemporal cortex (OTC), either in the RH or left hemisphere (LH), following early childhood resection for the management of drug-resistant epilepsy. The matched controls and those with a lesion outside of OTC evinced the standard superiority in processing upright over inverted faces and the reverse sensitivity to a nonface category (bicycles). In contrast, the LH and the RH patient groups were significantly less accurate than the controls and showed mild orientation sensitivities at best (and not always in the predicted directions). For the two patient groups, the accuracies of face and bicycle processing did not differ from each other and were not obviously related to performance on intermediate level global form tasks with, again, poorer thresholds for both patient groups than controls and no difference between the patient groups. These findings shed light on the complexity of hemispheric lateralization and face and nonface object processing in individuals following surgical resection of OTC. Overall, this study highlights the unique dynamics and potential for plasticity in those with childhood cortical resection.

Modeling Biological Face Recognition with Deep Convolutional Neural Networks

Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground, and recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional "face spaces." In this review, we summarize the first studies that use DCNNs to model biological face recognition. On the basis of a broad spectrum of behavioral and computational evidence, we conclude that DCNNs are useful models that closely resemble the general hierarchical organization of face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. First, studies on face detection in DCNNs indicate that elementary face selectivity emerges automatically through feedforward processing even in the absence of visual experience. Second, studies on face identification in DCNNs suggest that identity-specific experience and generative mechanisms facilitate this particular challenge. Taken together, as this novel modeling approach enables close control of predisposition (i.e., architecture) and experience (i.e., training data), it may be suited to inform long-standing debates on the substrates of biological face recognition.

High-dimensional topographic organization of visual features in the primate temporal lobe

The inferotemporal cortex supports our supreme object recognition ability. Numerous studies have been conducted to elucidate the functional organization of this brain area, but there are still important questions that remain unanswered, including how this organization differs between humans and non-human primates. Here, we use deep neural networks trained on object categorization to construct a 25-dimensional space of visual features, and systematically measure the spatial organization of feature preference in both male monkey brains and human brains using fMRI. These feature maps allow us to predict the selectivity of a previously unknown region in monkey brains, which is corroborated by additional fMRI and electrophysiology experiments. These maps also enable quantitative analyses of the topographic organization of the temporal lobe, demonstrating the existence of a pair of orthogonal gradients that differ in spatial scale and revealing significant differences in the functional organization of high-level visual areas between monkey and human brains.

Developmental emergence of holistic processing in word recognition

Holistic processing (HP) of faces refers to the obligatory, simultaneous processing of the parts and their relations, and it emerges over the course of development. HP is manifest in a decrement in the perception of inverted versus upright faces and a reduction in face processing ability when the relations between parts are perturbed. Here, adopting the HP framework for faces, we examined the developmental emergence of HP in another domain for which human adults have expertise, namely, visual word processing. Children, adolescents, and adults performed a lexical decision task and we used two established signatures of HP for faces: the advantage in perception of upright over inverted words and nonwords and the reduced sensitivity to increasing parts (word length). Relative to the other groups, children showed less of an advantage for upright versus inverted trials and lexical decision was more affected by increasing word length. Performance on these HP indices was strongly associated with age and with reading proficiency. Also, the emergence of HP for word perception was not simply a result of improved visual perception over the course of development as no group differences were observed on an object decision task. These results reveal the developmental emergence of HP for orthographic input, and reflect a further instance of experience-dependent tuning of visual perception. These results also add to existing findings on the commonalities of mechanisms of word and face recognition. RESEARCH HIGHLIGHTS: Children showed less of an advantage for upright versus inverted trials compared to adolescents and adults. Relative to the other groups, lexical decision in children was more affected by increasing word length. Performance on holistic processing (HP) indices was strongly associated with age and with reading proficiency. HP emergence for word perception was not due to improved visual perception over development as there were no group differences on an object decision task.

Cortical topographic motifs emerge in a self-organized map of object space

The human ventral visual stream has a highly systematic organization of object information, but the causal pressures driving these topographic motifs are highly debated. Here, we use self-organizing principles to learn a topographic representation of the data manifold of a deep neural network representational space. We find that a smooth mapping of this representational space showed many brain-like motifs, with a large-scale organization by animacy and real-world object size, supported by mid-level feature tuning, with naturally emerging face- and scene-selective regions. While some theories of the object-selective cortex posit that these differently tuned regions of the brain reflect a collection of distinctly specified functional modules, the present work provides computational support for an alternate hypothesis that the tuning and topography of the object-selective cortex reflect a smooth mapping of a unified representational space.

The neuroconnectionist research programme

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.

A Unifying Principle for the Functional Organization of Visual Cortex

A key feature of many cortical systems is functional organization: the arrangement of neurons with specific functional properties in characteristic spatial patterns across the cortical surface. However, the principles underlying the emergence and utility of functional organization are poorly understood. Here we develop the Topographic Deep Artificial Neural Network (TDANN), the first unified model to accurately predict the functional organization of multiple cortical areas in the primate visual system. We analyze the key factors responsible for the TDANN's success and find that it strikes a balance between two specific objectives: achieving a task-general sensory representation that is self-supervised, and maximizing the smoothness of responses across the cortical sheet according to a metric that scales relative to cortical surface area. In turn, the representations learned by the TDANN are lower dimensional and more brain-like than those in models that lack a spatial smoothness constraint. Finally, we provide evidence that the TDANN's functional organization balances performance with inter-area connection length, and use the resulting models for a proof-of-principle optimization of cortical prosthetic design. Our results thus offer a unified principle for understanding functional organization and a novel view of the functional role of the visual system in particular.

Using artificial neural networks to ask 'why' questions of minds and brains

Neuroscientists have long characterized the properties and functions of the nervous system, and are increasingly succeeding in answering how brains perform the tasks they do. But the question 'why' brains work the way they do is asked less often. The new ability to optimize artificial neural networks (ANNs) for performance on human-like tasks now enables us to approach these 'why' questions by asking when the properties of networks optimized for a given task mirror the behavioral and neural characteristics of humans performing the same task. Here we highlight the recent success of this strategy in explaining why the visual and auditory systems work the way they do, at both behavioral and neural levels.

Understanding Human Object Vision: A Picture Is Worth a Thousand Representations

Objects are the core meaningful elements in our visual environment. Classic theories of object vision focus upon object recognition and are elegant and simple. Some of their proposals still stand, yet the simplicity is gone. Recent evolutions in behavioral paradigms, neuroscientific methods, and computational modeling have allowed vision scientists to uncover the complexity of the multidimensional representational space that underlies object vision. We review these findings and propose that the key to understanding this complexity is to relate object vision to the full repertoire of behavioral goals that underlie human behavior, running far beyond object recognition. There might be no such thing as core object recognition, and if it exists, then its importance is more limited than traditionally thought.

Does the brain's ventral visual pathway compute object shape?

A rich behavioral literature has shown that human object recognition is supported by a representation of shape that is tolerant to variations in an object's appearance. Such 'global' shape representations are achieved by describing objects via the spatial arrangement of their local features, or structure, rather than by the appearance of the features themselves. However, accumulating evidence suggests that the ventral visual pathway - the primary substrate underlying object recognition - may not represent global shape. Instead, ventral representations may be better described as a basis set of local image features. We suggest that this evidence forces a reevaluation of the role of the ventral pathway in object perception and posits a broader network for shape perception that encompasses contributions from the dorsal pathway.

Improving the accuracy of single-trial fMRI response estimates using GLMsingle

Advances in artificial intelligence have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experiment. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Comparable improvements in reliability are also observed in a smaller-scale auditory dataset from the StudyForrest experiment. These improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. We demonstrate that GLMsingle: (i) helps decorrelate response estimates between trials nearby in time; (ii) enhances representational similarity between subjects within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets sampling brain activity across many experimental conditions.

Predictive coding is a consequence of energy efficiency in recurrent neural networks

Predictive coding is a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modeling to demonstrate that such architectural hardwiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimize their energy consumption while operating in predictive environments, the networks self-organize into prediction and error units with appropriate inhibitory and excitatory interconnections and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down-driven predictions, we demonstrate, via virtual lesioning experiments, that networks perform predictions on two timescales: fast lateral predictions among sensory units and slower prediction cycles that integrate evidence over time.

With childhood hemispherectomy, one hemisphere can support—but is suboptimal for—word and face recognition

The right and left cerebral hemispheres are important for face and word recognition, respectively—a specialization that emerges over human development. The question is whether this bilateral distribution is necessary or whether a single hemisphere, be it left or right, can support both face and word recognition. Here, face and word recognition accuracy in patients (median age 16.7 y) with a single hemisphere following childhood hemispherectomy was compared against matched typical controls. In experiment 1, participants viewed stimuli in central vision. Across both face and word tasks, accuracy of both left and right hemispherectomy patients, while significantly lower than controls' accuracy, averaged above 80% and did not differ from each other. To compare patients' single hemisphere more directly to one hemisphere of controls, in experiment 2, participants viewed stimuli in one visual field to constrain initial processing chiefly to a single (contralateral) hemisphere. Whereas controls had higher word accuracy when words were presented to the right than to the left visual field, there was no field/hemispheric difference for faces. In contrast, left and right hemispherectomy patients, again, showed comparable performance to one another on both face and word recognition, albeit significantly lower than controls. Altogether, the findings indicate that a single developing hemisphere, either left or right, may be sufficiently plastic for comparable representation of faces and words. However, perhaps due to increased competition or “neural crowding,” constraining cortical representations to one hemisphere may collectively hamper face and word recognition, relative to that observed in typical development with two hemispheres.

The Dorsal Visual Pathway Represents Object-Centered Spatial Relations for Object Recognition

Although there is mounting evidence that input from the dorsal visual pathway is crucial for object processes in the ventral pathway, the specific functional contributions of dorsal cortex to these processes remain poorly understood. Here, we hypothesized that dorsal cortex computes the spatial relations among an object's parts, a process crucial for forming global shape percepts, and transmits this information to the ventral pathway to support object categorization. Using fMRI with human participants (females and males), we discovered regions in the intraparietal sulcus (IPS) that were selectively involved in computing object-centered part relations. These regions exhibited task-dependent functional and effective connectivity with ventral cortex, and were distinct from other dorsal regions, such as those representing allocentric relations, 3D shape, and tools. In a subsequent experiment, we found that the multivariate response of posterior (p)IPS, defined on the basis of part-relations, could be used to decode object category at levels comparable to ventral object regions. Moreover, mediation and multivariate effective connectivity analyses further suggested that IPS may account for representations of part relations in the ventral pathway. Together, our results highlight specific contributions of the dorsal visual pathway to object recognition. We suggest that dorsal cortex is a crucial source of input to the ventral pathway and may support the ability to categorize objects on the basis of global shape.SIGNIFICANCE STATEMENT Humans categorize novel objects rapidly and effortlessly. Such categorization is achieved by representing an object's global shape structure, that is, the relations among object parts. Yet, despite their importance, it is unclear how part relations are represented neurally. Here, we hypothesized that object-centered part relations may be computed by the dorsal visual pathway, which is typically implicated in visuospatial processing. Using fMRI, we identified regions selective for the part relations in dorsal cortex. We found that these regions can support object categorization, and even mediate representations of part relations in the ventral pathway, the region typically thought to support object categorization. Together, these findings shed light on the broader network of brain regions that support object categorization.

A neural population selective for song in human auditory cortex

How is music represented in the brain? While neuroimaging has revealed some spatial segregation between responses to music versus other sounds, little is known about the neural code for music itself. To address this question, we developed a method to infer canonical response components of human auditory cortex using intracranial responses to natural sounds, and further used the superior coverage of fMRI to map their spatial distribution. The inferred components replicated many prior findings, including distinct neural selectivity for speech and music, but also revealed a novel component that responded nearly exclusively to music with singing. Song selectivity was not explainable by standard acoustic features, was located near speech- and music-selective responses, and was also evident in individual electrodes. These results suggest that representations of music are fractionated into subpopulations selective for different types of music, one of which is specialized for the analysis of song.

Face perception: computational insights from phylogeny

Studies of face perception in primates elucidate the psychological and neural mechanisms that support this critical and complex ability. Recent progress in characterizing face perception across species, for example in insects and reptiles, has highlighted the ubiquity over phylogeny of this key ability for social interactions and survival. Here, we review the competence in face perception across species and the types of computation that support this behavior. We conclude that the computational complexity of face perception evinced by a species is not related to phylogenetic status and is, instead, largely a product of environmental context and social and adaptive pressures. Integrating findings across evolutionary data permits the derivation of computational principles that shed further light on primate face perception.