Segregation of feedforward and feedback projections in mouse visual cortex

Layer 6 corticocortical neurons are a major route for intra- and interhemispheric feedback

The neocortex comprises anatomically discrete yet interconnected areas that are symmetrically located across the two hemispheres. Determining the logic of these macrocircuits is necessary for understanding high level brain function. Here in mice, we have mapped the areal and laminar organization of the ipsi- and contralateral cortical projection onto the primary visual, somatosensory, and motor cortices. We find that although the ipsilateral hemisphere is the primary source of cortical input, there is substantial contralateral symmetry regarding the relative contribution and areal identity of input. Laminar analysis of these input areas show that excitatory Layer 6 corticocortical cells (L6 CCs) are a major projection pathway within and between the two hemispheres. Analysis of the relative contribution of inputs from supra- (feedforward) and infragranular (feedback) layers reveals that contra-hemispheric projections reflect a dominant feedback organization compared to their ipsi-cortical counterpart. The magnitude of the interhemispheric difference in hierarchy was largest for sensory and motor projection areas compared to frontal, medial, or lateral brain areas due to a proportional increase in input from L6 neurons. L6 CCs therefore not only mediate long-range cortical communication but also reflect its inherent feedback organization.

Spatial dynamics of spontaneous activity in the developing and adult cortices

Even in the absence of external stimuli, the brain remains remarkably active, with neurons continuously firing and communicating with each other. It is not merely random firing of individual neurons but rather orchestrated patterns of activity that propagate throughout the intricate network. Over two decades, advancements in neuroscience observation tools for hemodynamics, membrane potential, and neural calcium signals, have allowed researchers to analyze the dynamics of spontaneous activity across different spatial scales, from individual neurons to macroscale brain networks. One of the remarkable findings from these studies is that the spatial patterns of spontaneous activity in the developing brain are vastly different from those in the mature adult brain. Spatial patterns of spontaneous activity during development are essential for connection refinement between brain regions, whereas the functional role in the adult brain is still controversial. In this paper, I review the differences in spatial dynamics of spontaneous activity between developing and adult cortices. Then, I delve into the cellular mechanisms underlying spontaneous activity, especially its generation and propagation manner, to contribute to a deeper understanding of brain function and its development.

Visual experience reduces the spatial redundancy between cortical feedback inputs and primary visual cortex neurons

The role of experience in the organization of cortical feedback (FB) remains unknown. We measured the effects of manipulating visual experience on the retinotopic specificity of supragranular and infragranular projections from the lateromedial (LM) visual area to layer (L)1 of the mouse primary visual cortex (V1). LM inputs were, on average, retinotopically matched with V1 neurons in normally and dark-reared mice, but visual exposure reduced the fraction of spatially overlapping inputs to V1. FB inputs from L5 conveyed more surround information to V1 than those from L2/3. The organization of LM inputs from L5 depended on their orientation preference and was disrupted by dark rearing. These observations were recapitulated by a model where visual experience minimizes receptive field overlap between LM inputs and V1 neurons. Our results provide a mechanism for the dependency of surround modulations on visual experience and suggest how expected interarea coactivation patterns are learned in cortical circuits.

It's all in the timing: delayed feedback in autism may weaken predictive mechanisms during contour integration

Humans rely on predictive and integrative mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. Although initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through automatic conveyance of statistical probabilities based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP)-the IC-effect-that occurs over lateral occipital scalp during the timeframe of the visual N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6- to 17-yr-old children with ASD (n = 32) or NT development (n = 53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21 ms later in ASD, even though initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared with NT children. This delay in the feedback-dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by visual feedback.NEW & NOTEWORTHY Children with autism often present with an atypical visual perceptual style that emphasizes parts or details over the whole. Using electroencephalography (EEG), this study identifies delays in the visual feedback from higher-order sensory brain areas to primary sensory regions. Because this type of visual feedback is thought to carry information about prior sensory experiences, individuals with autism may have difficulty efficiently using prior experience or putting together parts into a whole to help make sense of incoming new visual information. This provides empirical neural evidence to support theories of disrupted sensory perception mechanisms in autism.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

It's all in the timing: Delayed feedback in autism may weaken predictive mechanisms during contour integration

Humans rely on predictive mechanisms during visual processing to efficiently resolve incomplete or ambiguous sensory signals. While initial low-level sensory data are conveyed by feedforward connections, feedback connections are believed to shape sensory processing through conveyance of statistical predictions based on prior exposure to stimulus configurations. Individuals with autism spectrum disorder (ASD) show biases in stimulus processing toward parts rather than wholes, suggesting their sensory processing may be less shaped by statistical predictions acquired through prior exposure to global stimulus properties. Investigations of illusory contour (IC) processing in neurotypical (NT) adults have established a well-tested marker of contour integration characterized by a robust modulation of the visually evoked potential (VEP) - the IC-effect - that occurs over lateral occipital scalp during the timeframe of the N1 component. Converging evidence strongly supports the notion that this IC-effect indexes a signal with significant feedback contributions. Using high-density VEPs, we compared the IC-effect in 6-17-year-old children with ASD (n=32) or NT development (n=53). Both groups of children generated an IC-effect that was equivalent in amplitude. However, the IC-effect notably onset 21ms later in ASD, even though timing of initial VEP afference was identical across groups. This suggests that feedforward information predominated during perceptual processing for 15% longer in ASD compared to NT children. This delay in the feedback dependent IC-effect, in the context of known developmental differences between feedforward and feedback fibers, suggests a potential pathophysiological mechanism of visual processing in ASD, whereby ongoing stimulus processing is less shaped by statistical prediction mechanisms.

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

The fractal brain: scale-invariance in structure and dynamics

The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.

NILRNN: A Neocortex-Inspired Locally Recurrent Neural Network for Unsupervised Feature Learning in Sequential Data

Unsupervised feature learning refers to the problem of learning useful feature extraction functions from unlabeled data. Despite the great success of deep learning networks in this task in recent years, both for static and for sequential data, these systems can in general still not compete with the high performance of our brain at learning to extract useful representations from its sensory input. We propose the Neocortex-Inspired Locally Recurrent Neural Network: a new neural network for unsupervised feature learning in sequential data that brings ideas from the structure and function of the neocortex to the well-established fields of machine learning and neural networks. By mimicking connection patterns in the feedforward circuits of the neocortex, our system tries to generalize some of the ideas behind the success of convolutional neural networks to types of data other than images. To evaluate the performance of our system at extracting useful features, we have trained different classifiers using those and other learnt features as input and we have compared the obtained accuracies. Our system has shown to outperform other shallow feature learning systems in this task, both in terms of the accuracies achieved and in terms of how fast the classification task is learnt. The results obtained confirm our system as a state-of-the-art shallow feature learning system for sequential data, and suggest that extending it to or integrating it into deep architectures may lead to new successful networks that are competent at dealing with complex sequential tasks.

Contextual drive of neuronal responses in mouse V1 in the absence of feedforward input

Neurons in the primary visual cortex (V1) respond to stimuli in their receptive field (RF), which is defined by the feedforward input from the retina. However, V1 neurons are also sensitive to contextual information outside their RF, even if the RF itself is unstimulated. Here, we examined the cortical circuits for V1 contextual responses to gray disks superimposed on different backgrounds. Contextual responses began late and were strongest in the feedback-recipient layers of V1. They differed between the three main classes of inhibitory neurons, with particularly strong contextual drive of VIP neurons, indicating a contribution of disinhibitory circuits to contextual drive. Contextual drive was strongest when the gray disk was perceived as figure, occluding its background, rather than a hole. Our results link contextual drive in V1 to perceptual organization and provide previously unknown insight into how recurrent processing shapes the response of sensory neurons to facilitate figure perception.

Distributed context-dependent choice information in mouse posterior cortex

Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex-traditionally implicated in decision computations-the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals' attention state. A recurrent neural network trained with animals' choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal-environment interactions.

Thalamocortical circuits for the formation of hierarchical pathways in the mammalian visual cortex

External sensory inputs propagate from lower-order to higher-order brain areas, and the hierarchical neural network supporting this information flow is a fundamental structure of the mammalian brain. In the visual system, multiple hierarchical pathways process different features of the visual information in parallel. The brain can form this hierarchical structure during development with few individual differences. A complete understanding of this formation mechanism is one of the major goals of neuroscience. For this purpose, it is necessary to clarify the anatomical formation process of connections between individual brain regions and to elucidate the molecular and activity-dependent mechanisms that instruct these connections in each areal pair. Over the years, researchers have unveiled developmental mechanisms of the lower-order pathway from the retina to the primary visual cortex. The anatomical formation of the entire visual network from the retina to the higher visual cortex has recently been clarified, and higher-order thalamic nuclei are gaining attention as key players in this process. In this review, we summarize the network formation process in the mouse visual system, focusing on projections from the thalamic nuclei to the primary and higher visual cortices, which are formed during the early stages of development. Then, we discuss how spontaneous retinal activity that propagates through thalamocortical pathways is essential for the formation of corticocortical connections. Finally, we discuss the possible role of higher-order thalamocortical projections as template structures in the functional maturation of visual pathways that process different visual features in parallel.

Rapid development of motion-streak coding in the mouse visual cortex

Despite its importance, the development of higher visual areas (HVAs) at the cellular resolution remains largely unknown. Here, we conducted 2-photon calcium imaging of mouse HVAs lateromedial (LM) and anterolateral (AL) and V1 to observe developmental changes in visual response properties. HVA neurons showed selectivity for orientations and directions similar to V1 neurons at eye opening, which became sharper in the following weeks. Neurons in all areas over all developmental stages tended to respond selectively to dots moving along an axis perpendicular to their preferred orientation at slow speeds, suggesting a certain level of conventional motion coding already at eye opening. In contrast, at high speeds, many neurons responded to dots moving along the axis parallel to the preferred orientation in older animals but rarely after eye opening, indicating a lack of motion-streak coding in the earlier stage. Together, our results uncover the development of visual properties in HVAs.

Modular strategy for development of the hierarchical visual network in mice

Hierarchical and parallel networks are fundamental structures of the mammalian brain^1-8. During development, lower- and higher-order thalamic nuclei and many cortical areas in the visual system form interareal connections and build hierarchical dorsal and ventral streams^9-13. One hypothesis for the development of visual network wiring involves a sequential strategy wherein neural connections are sequentially formed alongside hierarchical structures from lower to higher areas^14-17. However, this sequential strategy would be inefficient for building the entire visual network comprising numerous interareal connections. We show that neural pathways from the mouse retina to primary visual cortex (V1) or dorsal/ventral higher visual areas (HVAs) through lower- or higher-order thalamic nuclei form as parallel modules before corticocortical connections. Subsequently, corticocortical connections among V1 and HVAs emerge to combine these modules. Retina-derived activity propagating the initial parallel modules is necessary to establish retinotopic inter-module connections. Thus, the visual network develops in a modular manner involving initial establishment of parallel modules and their subsequent concatenation. Findings in this study raise the possibility that parallel modules from higher-order thalamic nuclei to HVAs act as templates for cortical ventral and dorsal streams and suggest that the brain has an efficient strategy for the development of a hierarchical network comprising numerous areas.

Evaluation of advances in cortical development using model systems

Compared with that of even the closest primates, the human cortex displays a high degree of specialization and expansion that largely emerges developmentally. Although decades of research in the mouse and other model systems has revealed core tenets of cortical development that are well preserved across mammalian species, small deviations in transcription factor expression, novel cell types in primates and/or humans, and unique cortical architecture distinguish the human cortex. Importantly, many of the genes and signaling pathways thought to drive human-specific cortical expansion also leave the brain vulnerable to disease, as the misregulation of these factors is highly correlated with neurodevelopmental and neuropsychiatric disorders. However, creating a comprehensive understanding of human-specific cognition and disease remains challenging. Here, we review key stages of cortical development and highlight known or possible differences between model systems and the developing human brain. By identifying the developmental trajectories that may facilitate uniquely human traits, we highlight open questions in need of approaches to examine these processes in a human context and reveal translatable insights into human developmental disorders.

Postnatal Development of Centrifugal Inputs to the Olfactory Bulb

Processing in primary sensory areas is influenced by centrifugal inputs from higher brain areas, providing information about behavioral state, attention, or context. Activity in the olfactory bulb (OB), the first central processing stage of olfactory information, is dynamically modulated by direct projections from a variety of areas in adult mice. Despite the early onset of olfactory sensation compared to other senses, the development of centrifugal inputs to the OB remains largely unknown. Using retrograde tracing across development, we show that centrifugal projections to the OB are established during the postnatal period in an area-specific manner. While feedback projections from the piriform cortex (PIR) are already present shortly after birth, they strongly increase in number during postnatal development with an anterior-posterior gradient. Contralateral projections from the anterior olfactory nucleus (AON) are present at birth but only appeared postnatally for the nucleus of the lateral olfactory tract (nLOT). Numbers of OB projecting neurons from the lateral entorhinal cortex (LEC), ventral hippocampus, and cortical amygdala (CoA) show a sudden increase at the beginning of the second postnatal week and a delayed development compared to more anterior areas. These anatomical data suggest that limited top-down influence on odor processing in the OB may be present at birth, but strongly increases during postnatal development and is only fully established later in life.

Multi-regional module-based signal transmission in mouse visual cortex

The visual cortex is hierarchically organized, yet the presence of extensive recurrent and parallel pathways make it challenging to decipher how signals flow between neuronal populations. Here, we tracked the flow of spiking activity recorded from six interconnected levels of the mouse visual hierarchy. By analyzing leading and lagging spike-timing relationships among pairs of simultaneously recorded neurons, we created a cellular-scale directed network graph. Using a module-detection algorithm to cluster neurons based on shared connectivity patterns, we uncovered two multi-regional communication modules distributed across the hierarchy. The direction of signal flow both between and within these modules, differences in layer and area distributions, and distinct temporal dynamics suggest that one module transmits feedforward sensory signals, whereas the other integrates inputs for recurrent processing. These results suggest that multi-regional functional modules may be a fundamental feature of organization beyond cortical areas that supports signal propagation across hierarchical recurrent networks.

Projections of the Mouse Primary Visual Cortex

Lesion or damage to the primary visual cortex (V1) results in a profound loss of visual perception in humans. Similarly, in mice, optogenetic silencing of V1 profoundly impairs discrimination of orientated gratings. V1 is thought to have such a critical role in perception in part due to its position in the visual processing hierarchy. It is the first brain area in the neocortex to receive visual input, and it distributes this information to more than 18 brain areas. Here I review recent advances in our understanding of the organization and function of the V1 projections in the mouse. This progress is in part due to new anatomical and viral techniques that allow for efficient labeling of projection neurons. In the final part of the review, I conclude by highlighting challenges and opportunities for future research.

Higher Order Visual Areas Enhance Stimulus Responsiveness in Mouse Primary Visual Cortex

Over the past few years, the various areas that surround the primary visual cortex (V1) in the mouse have been associated with many functions, ranging from higher order visual processing to decision-making. Recently, some studies have shown that higher order visual areas influence the activity of the primary visual cortex, refining its processing capabilities. Here, we studied how in vivo optogenetic inactivation of two higher order visual areas with different functional properties affects responses evoked by moving bars in the primary visual cortex. In contrast with the prevailing view, our results demonstrate that distinct higher order visual areas similarly modulate early visual processing. In particular, these areas enhance stimulus responsiveness in the primary visual cortex, by more strongly amplifying weaker compared with stronger sensory-evoked responses (for instance specifically amplifying responses to stimuli not moving along the direction preferred by individual neurons) and by facilitating responses to stimuli entering the receptive field of single neurons. Such enhancement, however, comes at the expense of orientation and direction selectivity, which increased when the selected higher order visual areas were inactivated. Thus, feedback from higher order visual areas selectively amplifies weak sensory-evoked V1 responses, which may enable more robust processing of visual stimuli.

Wiring of higher-order cortical areas: Spatiotemporal development of cortical hierarchy

A hierarchical development of cortical areas was suggested over a century ago, but the diversity and complexity of cortical hierarchy properties have so far prevented a formal demonstration. The aim of this review is to clarify the similarities and differences in the developmental processes underlying cortical development of primary and higher-order areas. We start by recapitulating the historical and recent advances underlying the biological principle of cortical hierarchy in adults. We then revisit the arguments for a hierarchical maturation of cortical areas, and further integrate the principles of cortical areas specification during embryonic and postnatal development. We highlight how the dramatic expansion in cortical size might have contributed to the increased number of association areas sustaining cognitive complexification in evolution. Finally, we summarize the recent observations of an alteration of cortical hierarchy in neuropsychiatric disorders and discuss their potential developmental origins.

Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks

Hierarchy is a major organizational principle of the cortex and underscores modern computational theories of cortical function. The local microcircuit amplifies long-distance inter-areal input, which show distance-dependent changes in their laminar profiles. Statistical modeling of these changes in laminar profiles demonstrates that inputs from multiple hierarchical levels to their target areas show remarkable consistency, allowing the construction of a cortical hierarchy based on a principle of hierarchical distance. The statistical modeling that is applied to structure can also be applied to laminar differences in the oscillatory coherence between areas thereby determining a functional hierarchy of the cortex. Close examination of the anatomy of inter-areal connectivity reveals a dual counterstream architecture with well-defined distance-dependent feedback and feedforward pathways in both the supra- and infragranular layers, suggesting a multiplicity of feedback pathways with well-defined functional properties. These findings are consistent with feedback connections providing a generative network involved in a wide range of cognitive functions. A dynamical model constrained by connectivity data sheds insight into the experimentally observed signatures of frequency-dependent Granger causality for feedforward versus feedback signaling. Concerted experiments capitalizing on recent technical advances and combining tract-tracing, high-resolution fMRI, optogenetics and mathematical modeling hold the promise of a much improved understanding of lamina-constrained mechanisms of neural computation and cognition. However, because inter-areal interactions involve cortical layers that have been the target of important evolutionary changes in the primate lineage, these investigations will need to include human and non-human primate comparisons.

The Emergence of Hierarchical Somatosensory Processing in Late Prematurity

The somatosensory system has a hierarchical organization. Information processing increases in complexity from the contralateral primary sensory cortex to bilateral association cortices and this is represented by a sequence of somatosensory-evoked potentials recorded with scalp electroencephalographies. The mammalian somatosensory system matures over the early postnatal period in a rostro-caudal progression, but little is known about the development of hierarchical information processing in the human infant brain. To investigate the normal human development of the somatosensory hierarchy, we recorded potentials evoked by mechanical stimulation of hands and feet in 34 infants between 34 and 42 weeks corrected gestational age, with median postnatal age of 3 days. We show that the shortest latency potential was evoked for both hands and feet at all ages with a contralateral somatotopic source in the primary somatosensory cortex (SI). However, the longer latency responses, localized in SI and beyond, matured with age. They gradually emerged for the foot and, although always present for the hand, showed a shift from purely contralateral to bilateral hemispheric activation. These results demonstrate the rostro-caudal development of human somatosensory hierarchy and suggest that the development of its higher tiers is complete only just before the time of normal birth.

Long-range connections enrich cortical computations

The cerebral cortex can perform powerful computations, including those involved in higher cognitive functions. Cortical processing for such computations is executed by local circuits and is further enriched by long-range connectivity. This connectivity is activated under specific conditions and modulates local processing, providing flexibility in the computational performance of the cortex. For instance, long-range connectivity in the primary visual cortex exerts facilitatory impacts when the cortex is silent but suppressive impacts when the cortex is strongly sensory-stimulated. These dual impacts can be captured by a divisive gain control model. Recent methodological advances such as optogenetics, anatomical tracing, and two-photon microscopy have enabled neuroscientists to probe the circuit and synaptic bases of long-range connectivity in detail. Here, I review a series of evidence indicating essential roles of long-range connectivity in visual and hierarchical processing involving numerous cortical areas. I also describe an overview of the challenges encountered in investigating underlying synaptic mechanisms and highlight recent technical approaches that may overcome these difficulties and provide new insights into synaptic mechanisms for cortical processing involving long-range connectivity.

Evaluating the neurophysiological evidence for predictive processing as a model of perception

For many years, the dominant theoretical framework guiding research into the neural origins of perceptual experience has been provided by hierarchical feedforward models, in which sensory inputs are passed through a series of increasingly complex feature detectors. However, the long-standing orthodoxy of these accounts has recently been challenged by a radically different set of theories that contend that perception arises from a purely inferential process supported by two distinct classes of neurons: those that transmit predictions about sensory states and those that signal sensory information that deviates from those predictions. Although these predictive processing (PP) models have become increasingly influential in cognitive neuroscience, they are also criticized for lacking the empirical support to justify their status. This limited evidence base partly reflects the considerable methodological challenges that are presented when trying to test the unique predictions of these models. However, a confluence of technological and theoretical advances has prompted a recent surge in human and nonhuman neurophysiological research seeking to fill this empirical gap. Here, we will review this new research and evaluate the degree to which its findings support the key claims of PP.

Emerging Roles of Long Non-Coding RNAs as Drivers of Brain Evolution

Mammalian genomes encode tens of thousands of long-noncoding RNAs (lncRNAs), which are capable of interactions with DNA, RNA and protein molecules, thereby enabling a variety of transcriptional and post-transcriptional regulatory activities. Strikingly, about 40% of lncRNAs are expressed specifically in the brain with precisely regulated temporal and spatial expression patterns. In stark contrast to the highly conserved repertoire of protein-coding genes, thousands of lncRNAs have newly appeared during primate nervous system evolution with hundreds of human-specific lncRNAs. Their evolvable nature and the myriad of potential functions make lncRNAs ideal candidates for drivers of human brain evolution. The human brain displays the largest relative volume of any animal species and the most remarkable cognitive abilities. In addition to brain size, structural reorganization and adaptive changes represent crucial hallmarks of human brain evolution. lncRNAs are increasingly reported to be involved in neurodevelopmental processes suggested to underlie human brain evolution, including proliferation, neurite outgrowth and synaptogenesis, as well as in neuroplasticity. Hence, evolutionary human brain adaptations are proposed to be essentially driven by lncRNAs, which will be discussed in this review.

Development of Center-Surround Suppression in Infant Motion Processing

Motion direction of a large high-contrast pattern is more difficult to perceive than that of a small one [1]. This counterintuitive perceptual phenomenon is considered to reflect surround suppression, a receptive field property observed in the visual cortex [2-5]. Here, we demonstrate that this phenomenon can be observed in human infants. Infants at 7 to 8 months of age showed higher sensitivity for a small motion stimulus than for a large one. However, infants under 6 months showed the opposite result; motion sensitivity was higher for a large stimulus. These results suggest that suppressive surround regions beyond classical receptive fields develop in the second half of the first year. Moreover, we examined the size of spatial summation in infants and found that the spatial summation area shrinks from 3 to 8 months of age. Our findings suggest that the summation area for motion is broad with no surround suppression in early infancy and that it narrows and acquires suppressive surround regions in the first year of life, which might reflect the developmental changes in the receptive field structure.

Control of synaptic plasticity in deep cortical networks

Humans and many other animals have an enormous capacity to learn about sensory stimuli and to master new skills. However, many of the mechanisms that enable us to learn remain to be understood. One of the greatest challenges of systems neuroscience is to explain how synaptic connections change to support maximally adaptive behaviour. Here, we provide an overview of factors that determine the change in the strength of synapses, with a focus on synaptic plasticity in sensory cortices. We review the influence of neuromodulators and feedback connections in synaptic plasticity and suggest a specific framework in which these factors can interact to improve the functioning of the entire network.

Precision in the development of neocortical architecture: From progenitors to cortical networks

Of all brain regions, the 6-layered neocortex has undergone the most dramatic changes in size and complexity during mammalian brain evolution. These changes, occurring in the context of a conserved set of organizational features that emerge through stereotypical developmental processes, are considered responsible for the cognitive capacities and sensory specializations represented within the mammalian clade. The modern experimental era of developmental neurobiology, spanning 6 decades, has deciphered a number of mechanisms responsible for producing the diversity of cortical neuron types, their precise connectivity and the role of gene by environment interactions. Here, experiments providing insight into the development of cortical projection neuron differentiation and connectivity are reviewed. This current perspective integrates discussion of classic studies and new findings, based on recent technical advances, to highlight an improved understanding of the neuronal complexity and precise connectivity of cortical circuitry. These descriptive advances bring new opportunities for studies related to the developmental origins of cortical circuits that will, in turn, improve the prospects of identifying pathogenic targets of neurodevelopmental disorders.

Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle

The architectonic type principle relates patterns of cortico-cortical connectivity to the relative architectonic differentiation of cortical regions. One mechanism through which the observed close relation between cortical architecture and connectivity may be established is the joint development of cortical areas and their connections in developmental time windows. Here, we describe a theoretical exploration of the possible mechanistic underpinnings of the architectonic type principle, by performing systematic computational simulations of cortical development. The main component of our in silico model was a developing two-dimensional cortical sheet, which was gradually populated by neurons that formed cortico-cortical connections. To assess different explanatory mechanisms, we varied the spatiotemporal trajectory of the simulated neurogenesis. By keeping the rules governing axon outgrowth and connection formation constant across all variants of simulated development, we were able to create model variants which differed exclusively by the specifics of when and where neurons were generated. Thus, all differences in the resulting connectivity were due to the variations in spatiotemporal growth trajectories. Our results demonstrated that a prescribed targeting of interareal connection sites was not necessary for obtaining a realistic replication of the experimentally observed relation between connection patterns and architectonic differentiation. Instead, we found that spatiotemporal interactions within the forming cortical sheet were sufficient if a small number of empirically well-grounded assumptions were met, namely planar, expansive growth of the cortical sheet around two points of origin as neurogenesis progressed, stronger architectonic differentiation of cortical areas for later neurogenetic time windows, and stochastic connection formation. Thus, our study highlights a potential mechanism of how relative architectonic differentiation and cortical connectivity become linked during development. We successfully predicted connectivity in two species, cat and macaque, from simulated cortico-cortical connection networks, which further underscored the general applicability of mechanisms through which the architectonic type principle can explain cortical connectivity in terms of the relative architectonic differentiation of cortical regions.

Organization of Neural Population Code in Mouse Visual System

The mammalian visual system consists of several anatomically distinct areas, layers, and cell types. To understand the role of these subpopulations in visual information processing, we analyzed neural signals recorded from excitatory neurons from various anatomical and functional structures. For each of 186 mice, one of six genetically tagged cell types and one of six visual areas were targeted while the mouse was passively viewing various visual stimuli. We trained linear classifiers to decode one of six visual stimulus categories with distinct spatiotemporal structures from the population neural activity. We found that neurons in both the primary visual cortex and secondary visual areas show varying degrees of stimulus-specific decodability, and neurons in superficial layers tend to be more informative about the stimulus categories. Additional decoding analyses of directional motion were consistent with these findings. We observed synergy in the population code of direction in several visual areas suggesting area-specific organization of information representation across neurons. These differences in decoding capacities shed light on the specialized organization of neural information processing across anatomically distinct subpopulations, and further establish the mouse as a model for understanding visual perception.

Asymmetric Direct Reciprocal Connections Between Primary Visual and Somatosensory Cortices of the Mouse

Several studies show direct connections between primary sensory cortices involved in multisensory integration. The purpose of this study is to understand the microcircuitry of the reciprocal connections between visual and somatosensory cortices. The laminar distribution of retrogradely labeled cell bodies in V1 and in the somatosensory cortex both in (S1BF) and outside (S1) the barrel field was studied to provide layer indices in order to determine whether the connections are of feedforward, feedback or lateral type. Single axons were reconstructed and the size of their swellings was stereologically sampled. The negative layer indices in S1 and S1BF and the layer index near zero in V1 indicate that the connection from S1BF to V1 is of feedback type while the opposite is of lateral type. The greater incidence of larger axonal swellings in the projection from V1 to S1BF strongly suggests that S1BF receives a stronger driver input from V1 and that S1BF inputs to V1 have a predominant modulatory influence.

Early sensory experience influences the development of multisensory thalamocortical and intracortical connections of primary sensory cortices

The nervous system integrates information from multiple senses. This multisensory integration already occurs in primary sensory cortices via direct thalamocortical and corticocortical connections across modalities. In humans, sensory loss from birth results in functional recruitment of the deprived cortical territory by the spared senses but the underlying circuit changes are not well known. Using tracer injections into primary auditory, somatosensory, and visual cortex within the first postnatal month of life in a rodent model (Mongolian gerbil) we show that multisensory thalamocortical connections emerge before corticocortical connections but mostly disappear during development. Early auditory, somatosensory, or visual deprivation increases multisensory connections via axonal reorganization processes mediated by non-lemniscal thalamic nuclei and the primary areas themselves. Functional single-photon emission computed tomography of regional cerebral blood flow reveals altered stimulus-induced activity and higher functional connectivity specifically between primary areas in deprived animals. Together, we show that intracortical multisensory connections are formed as a consequence of sensory-driven multisensory thalamocortical activity and that spared senses functionally recruit deprived cortical areas by an altered development of sensory thalamocortical and corticocortical connections. The functional-anatomical changes after early sensory deprivation have translational implications for the therapy of developmental hearing loss, blindness, and sensory paralysis and might also underlie developmental synesthesia.

Postnatal refinement of interareal feedforward projections in ferret visual cortex

We studied the postnatal refinement of feedforward (FF) projections from ferret V1 to multiple cortical targets during the period around eye opening. Our goal was to establish (a) whether the developmental refinement of FF projections parallels that of feedback (FB) cortical circuits, and (b) whether FF pathways from V1 to different target areas refine with a similar rate. We injected the tracer CTb into V1 of juvenile ferrets, and visualized the pattern of labeled axon terminals in extrastriate cortex. Bouton density of FF projections to target areas 18, 19, and 21 declined steadily from 4 to 8 weeks postnatal. However, in area Ssy this decline was delayed somewhat, not occurring until after 6 weeks. During this postnatal period, mean interbouton intervals along individual FF axons to all visual areas increased, and we observed a concomitant moderate decrease in axon density in areas 18, 21, and Ssy. These data suggest that FF circuits linking V1 to its main extrastriate targets remodel largely synchronously in the weeks following eye opening, that FF and FB cortical circuits share a broadly similar developmental timecourse, and that postnatal visual experience is critical for the refinement of both FF and FB cortical circuits.

Feature-Specific Organization of Feedback Pathways in Mouse Visual Cortex

Higher and lower cortical areas in the visual hierarchy are reciprocally connected [1]. Although much is known about how feedforward pathways shape receptive field properties of visual neurons, relatively little is known about the role of feedback pathways in visual processing. Feedback pathways are thought to carry top-down signals, including information about context (e.g., figure-ground segmentation and surround suppression) [2-5], and feedback has been demonstrated to sharpen orientation tuning of neurons in the primary visual cortex (V1) [6, 7]. However, the response characteristics of feedback neurons themselves and how feedback shapes V1 neurons' tuning for other features, such as spatial frequency (SF), remain largely unknown. Here, using a retrograde virus, targeted electrophysiological recordings, and optogenetic manipulations, we show that putatively feedback neurons in layer 5 (hereafter "L5 feedback") in higher visual areas, AL (anterolateral area) and PM (posteromedial area), display distinct visual properties in awake head-fixed mice. AL L5 feedback neurons prefer significantly lower SF (mean: 0.04 cycles per degree [cpd]) compared to PM L5 feedback neurons (0.15 cpd). Importantly, silencing AL L5 feedback reduced visual responses of V1 neurons preferring low SF (mean change in firing rate: -8.0%), whereas silencing PM L5 feedback suppressed responses of high-SF-preferring V1 neurons (-20.4%). These findings suggest that feedback connections from higher visual areas convey distinctly tuned visual inputs to V1 that serve to boost V1 neurons' responses to SF. Such like-to-like functional organization may represent an important feature of feedback pathways in sensory systems and in the nervous system in general.

A Laminar Organization for Selective Cortico-Cortical Communication

The neocortex is central to mammalian cognitive ability, playing critical roles in sensory perception, motor skills and executive function. This thin, layered structure comprises distinct, functionally specialized areas that communicate with each other through the axons of pyramidal neurons. For the hundreds of such cortico-cortical pathways to underlie diverse functions, their cellular and synaptic architectures must differ so that they result in distinct computations at the target projection neurons. In what ways do these pathways differ? By originating and terminating in different laminae, and by selectively targeting specific populations of excitatory and inhibitory neurons, these “interareal” pathways can differentially control the timing and strength of synaptic inputs onto individual neurons, resulting in layer-specific computations. Due to the rapid development in transgenic techniques, the mouse has emerged as a powerful mammalian model for understanding the rules by which cortical circuits organize and function. Here we review our understanding of how cortical lamination constrains long-range communication in the mammalian brain, with an emphasis on the mouse visual cortical network. We discuss the laminar architecture underlying interareal communication, the role of neocortical layers in organizing the balance of excitatory and inhibitory actions, and highlight the structure and function of layer 1 in mouse visual cortex.

What do we know about laminar connectivity?

In this brief review, I attempt an overview of the main components of anatomical laminar-level connectivity. These are: extrinsic outputs, excitatory and inhibitory intrinsic connectivity, and intrinsic inputs. Supporting data are biased from the visual system of nonhuman primates (NHPs), but I have drawn as much as possible from a broader span in order to treat the important issue of area-specific variability. In a second part, I briefly discuss laminar connectivity in the context of network organization (feedforward/feedback cortical connections, and the major types of corticothalamic connections). I also point out anatomical issues in need of clarification, including more systematic, whole brain coverage of tracer injections; more data on anterogradely labeled terminations; more complete, area-specific quantitative data about projection neurons, and quantitative data on terminal density and convergence. Postsynaptic targets are largely unknown, but their identification is essential for understanding the finer analysis and principles of laminar patterns. Laminar resolution MRI offers a promising new tool for exploring laminar connectivity: it is potentially fast and macro-scale, and allows for repeated investigation under different stimulus conditions. Conversely, anatomical resolution, although detailed beyond the current level of MRI visualization, offers a rich trove for experimental design and interpretation of fMRI activation patterns.

Neural Elements for Predictive Coding

Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).

An excitatory cortical feedback loop gates retinal wave transmission in rodent thalamus

Spontaneous retinal waves are critical for the development of receptive fields in visual thalamus (LGN) and cortex (VC). Despite a detailed understanding of the circuit specializations in retina that generate waves, whether central circuit specializations also exist to control their propagation through visual pathways of the brain is unknown. Here we identify a developmentally transient, corticothalamic amplification of retinal drive to thalamus as a mechanism for retinal wave transmission in the infant rat brain. During the period of retinal waves, corticothalamic connections excite LGN, rather than driving feedforward inhibition as observed in the adult. This creates an excitatory feedback loop that gates retinal wave transmission through the LGN. This cortical multiplication of retinal wave input ends just prior to eye-opening, as cortex begins to inhibit LGN. Our results show that the early retino-thalamo-cortical circuit uses developmentally specialized feedback amplification to ensure powerful, high-fidelity transmission of retinal activity despite immature connectivity.

DOI:http://dx.doi.org/10.7554/eLife.18816.001

The brain of a developing fetus has a big job to do: it needs to create the important connections between neurons that the individual will need later in life. This is a challenge because the first connections that form between neurons are sparse, weak and unreliable. They would not be expected to be able to transmit signals in a robust or effective way, and yet they do. How the nervous system solves this problem is an important question, because many neurological disorders may be the result of bad wiring between neurons in the fetal brain.

When an adult human or other mammal “sees” an object, visual information from the eye is transmitted to a part of the brain called the thalamus. From there it is sent on to another part of the brain called the cortex. The cortex also provides feedback to the thalamus to adjust the system and often acts as a brake in adults to limit the flow of information from the eyes.

Murata and Colonnese investigated whether the fetal brain contains any “booster” circuits of neurons that can amplify weak signals from other neurons to help ensure that information is transferred accurately. The experiments monitored and altered visual activity in the brains of newborn rats – which have similar activity patterns to those observed in human babies born prematurely. Murata and Colonnese found that in these rats the feedback signals from the cortex to the thalamus actually multiply the visual signals from the eye, instead of restraining them. This causes a massive amplification in activity in the developing brain and explains how the fetal brain stays active despite its neurons being only weakly connected.

The booster circuit stops working just before the eyes first open (equivalent to birth in humans) as the connections between neurons become stronger, and is replaced by the braking mechanism seen in adults. This is important, because continued amplification of signals in the adult brain might cause excessive brain activity and epilepsy. The findings of Murata and Colonnese may therefore help to explain why epileptic seizures have different causes and behave differently in children and adults.

The next step following on from this work is to find out how the braking mechanism forms in young animals. Future studies will also focus on understanding the precise role the booster circuit plays in early brain development.

DOI:http://dx.doi.org/10.7554/eLife.18816.002

Toward an Integration of Deep Learning and Neuroscience

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

The Computational Properties of a Simplified Cortical Column Model

The mammalian neocortex has a repetitious, laminar structure and performs functions integral to higher cognitive processes, including sensory perception, memory, and coordinated motor output. What computations does this circuitry subserve that link these unique structural elements to their function? Potjans and Diesmann (2014) parameterized a four-layer, two cell type (i.e. excitatory and inhibitory) model of a cortical column with homogeneous populations and cell type dependent connection probabilities. We implement a version of their model using a displacement integro-partial differential equation (DiPDE) population density model. This approach, exact in the limit of large homogeneous populations, provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials. It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics. We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties. When inputs are constrained to jointly and equally target excitatory and inhibitory neurons, we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals. One of these, a simple subtractive operation, can act as an error signal passed between hierarchical processing stages.

What computations do existing biophysically-plausible models of cortex perform on their inputs, and how do these computations relate to theories of cortical processing? We begin with a computational model of cortical tissue and seek to understand its input/output transformations. Our approach limits confirmation bias, and differs from a more constructionist approach of starting with a computational theory and then creating a model that can implement its necessary features. We here choose a population-level modeling technique that does not sacrifice accuracy, as it well-approximates the mean firing-rate of a population of leaky integrate-and-fire neurons. We extend this approach to simulate recurrently coupled neural populations, and characterize the computational properties of the Potjans and Diesmann cortical column model. We find that this model is capable of computing linear operations and naturally generates a subtraction operation implicated in theories of predictive coding. Although our quantitative findings are restricted to this particular model, we demonstrate that these conclusions are not highly sensitive to the model parameterization.

Population Response Propagation to Extrastriate Areas Evoked by Intracortical Electrical Stimulation in V1

The mouse visual system has multiple extrastriate areas surrounding V1 each with a distinct representation of the visual field and unique functional and connectivity profiles, which are believed to form two parallel processing streams, similar to the ventral and dorsal streams in primates. At the same time, mouse visual areas have a high degree of interconnectivity, in particular V1 sends input to all higher visual areas. The study of these direct connections can further our understanding of the cortical processing of visual signals in the early mammalian cortex. Several studies have been published about the anatomy of these connections, but an in vivo electrophysiological characterization and comparison of the transmission to multiple extrastriate areas has not yet been reported. We used intracortical electrical stimulation combined with RH1691 VSD imaging in adult C57BL/6 mice in urethane anesthesia to analyze interareal transmission from V1 to extrastriate areas in superficial cortical layers. We found seven extrastriate response sites (five lateral, two medial) in a spatial pattern similar to area maps of the mouse visual cortex and, by shifting the location of V1 stimulation, demonstrated that the evoked responses in LM and AL were in accordance with the visuotopic mappings of these areas known from anatomy and in vivo studies. These two sites, considered to be gateways to their processing streams, had shorter latencies and faster transmission speeds than other extrastriate response sites. Short latency differences between response sites, and that TTX injection into LM reduced but did not eliminate other extrastriate responses indicated that the evoked cortical activity was, at least partially, transmitted directly from V1 to extrastriate areas. This study reports on analysis of interareal transmission from V1 to multiple extrastriate areas in mouse using intracortical electrical stimulation in vivo.

The outer subventricular zone and primate-specific cortical complexification

Evolutionary expansion and complexification of the primate cerebral cortex are largely linked to the emergence of the outer subventricular zone (OSVZ), a uniquely structured germinal zone that generates the expanded primate supragranular layers. The primate OSVZ departs from rodent germinal zones in that it includes a higher diversity of precursor types, inter-related in bidirectional non-hierarchical lineages. In addition, primate-specific regulatory mechanisms are operating in primate cortical precursors via the occurrence of novel miRNAs. Here, we propose that the origin and evolutionary importance of the OSVZ is related to genetic changes in multiple regulatory loops and that cell-cycle regulation is a favored target for evolutionary adaptation of the cortex.

The neocortical circuit: themes and variations

Similarities in neocortical circuit organization across areas and species suggest a common strategy to process diverse types of information, including sensation from diverse modalities, motor control and higher cognitive processes. Cortical neurons belong to a small number of main classes. The properties of these classes, including their local and long-range connectivity, developmental history, gene expression, intrinsic physiology and in vivo activity patterns, are remarkably similar across areas. Each class contains subclasses; for a rapidly growing number of these, conserved patterns of input and output connections are also becoming evident. The ensemble of circuit connections constitutes a basic circuit pattern that appears to be repeated across neocortical areas, with area- and species-specific modifications. Such 'serially homologous' organization may adapt individual neocortical regions to the type of information each must process.

The stimulus selectivity and connectivity of layer six principal cells reveals cortical microcircuits underlying visual processing

Sensory computations performed in the neocortex involve layer six (L6) cortico-cortical (CC) and cortico-thalamic (CT) signaling pathways. Developing an understanding of the physiological role of these circuits requires dissection of the functional specificity and connectivity of the underlying individual projection neurons. By combining whole-cell recording from identified L6 principal cells in the mouse primary visual cortex (V1) with modified rabies virus-based input mapping, we have determined the sensory response properties and upstream monosynaptic connectivity of cells mediating the CC or CT pathway. We show that CC-projecting cells encompass a broad spectrum of selectivity to stimulus orientation and are predominantly innervated by deep layer V1 neurons. In contrast, CT-projecting cells are ultrasparse firing, exquisitely tuned to orientation and direction information, and receive long-range input from higher cortical areas. This segregation in function and connectivity indicates that L6 microcircuits route specific contextual and stimulus-related information within and outside the cortical network.

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L6 cortico-cortical neurons are broadly tuned to stimulus orientation

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L6 cortico-thalamic neurons are sparse firing and highly tuned to stimulus orientation

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L6 cortico-cortical neurons receive input from cells located mostly within V1

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L6 cortico-thalamic neurons receive input from higher order cortical areas

Developmental remodeling of corticocortical feedback circuits in ferret visual cortex

Visual cortical areas in the mammalian brain are linked through a system of interareal feedforward and feedback connections, which presumably underlie different visual functions. We characterized the refinement of feedback projections to primary visual cortex (V1) from multiple sources in juvenile ferrets ranging in age from 4-10 weeks postnatal. We studied whether the refinement of different aspects of feedback circuitry from multiple visual cortical areas proceeds at a similar rate in all areas. We injected the neuronal tracer cholera toxin B (CTb) into V1 and mapped the areal and laminar distribution of retrogradely labeled cells in extrastriate cortex. Around the time of eye opening at 4 weeks postnatal, the retinotopic arrangement of feedback appears essentially adult-like; however, suprasylvian cortex supplies the greatest proportion of feedback, whereas area 18 supplies the greatest proportion in the adult. The density of feedback cells and the ratio of supragranular/infragranular feedback contribution declined in this period at a similar rate in all cortical areas. We also found significant feedback to V1 from layer IV of all extrastriate areas. The regularity of cell spacing, the proportion of feedback arising from layer IV, and the tangential extent of feedback in each area all remained essentially unchanged during this period, except for the infragranular feedback source in area 18, which expanded. Thus, while much of the basic pattern of cortical feedback to V1 is present before eye opening, there is major synchronous reorganization after eye opening, suggesting a crucial role for visual experience in this remodeling process.

Distinct balance of excitation and inhibition in an interareal feedforward and feedback circuit of mouse visual cortex

Mouse visual cortex is subdivided into multiple distinct, hierarchically organized areas that are interconnected through feedforward (FF) and feedback (FB) pathways. The principal synaptic targets of FF and FB axons that reciprocally interconnect primary visual cortex (V1) with the higher lateromedial extrastriate area (LM) are pyramidal cells (Pyr) and parvalbumin (PV)-expressing GABAergic interneurons. Recordings in slices of mouse visual cortex have shown that layer 2/3 Pyr cells receive excitatory monosynaptic FF and FB inputs, which are opposed by disynaptic inhibition. Most notably, inhibition is stronger in the FF than FB pathway, suggesting pathway-specific organization of feedforward inhibition (FFI). To explore the hypothesis that this difference is due to diverse pathway-specific strengths of the inputs to PV neurons we have performed subcellular Channelrhodopsin-2-assisted circuit mapping in slices of mouse visual cortex. Whole-cell patch-clamp recordings were obtained from retrobead-labeled FF(V1→LM)- and FB(LM→V1)-projecting Pyr cells, as well as from tdTomato-expressing PV neurons. The results show that the FF(V1→LM) pathway provides on average 3.7-fold stronger depolarizing input to layer 2/3 inhibitory PV neurons than to neighboring excitatory Pyr cells. In the FB(LM→V1) pathway, depolarizing inputs to layer 2/3 PV neurons and Pyr cells were balanced. Balanced inputs were also found in the FF(V1→LM) pathway to layer 5 PV neurons and Pyr cells, whereas FB(LM→V1) inputs to layer 5 were biased toward Pyr cells. The findings indicate that FFI in FF(V1→LM) and FB(LM→V1) circuits are organized in a pathway- and lamina-specific fashion.

A modulatory effect of the feedback from higher visual areas to V1 in the mouse

Using a mouse brain slice preparation, we studied the modulatory effects of a feedback projection from higher visual cortical areas, mostly or exclusively area LM (or V2), on two inputs to layer 4 cells in the first visual area (V1). The two inputs to these cells were geniculocortical and an unspecified intracortical input, possibly involving layer 6 cells. We found that activation of metabotropic glutamate receptors (mGluRs) from stimulation of the feedback projection reduced the evoked excitatory postsynaptic currents of both of these inputs to layer 4 but that this modulation acts in an input-specific way. Reducing the strength of the geniculocortical input in adults involved both presynaptic and postsynaptic group I mGluRs (although in younger animals presynaptic group II mGluRs were also involved), whereas modulation of the intracortical input acted entirely via postsynaptic group II mGluRs. These results demonstrate that one of the effects of this feedback pathway is to control the gain of geniculocortical transmission.