How Does the Cerebral Cortex Work? Development, Learning, Attention, and 3-D Vision by Laminar Circuits of Visual Cortex

A neurodynamic model of the interaction between color perception and color memory

The memory color effect and Spanish castle illusion have been taken as evidence of the cognitive penetrability of vision. In the same manner, the successful decoding of color-related brain signals in functional neuroimaging studies suggests the retrieval of memory colors associated with a perceived gray object. Here, we offer an alternative account of these findings based on the design principles of adaptive resonance theory (ART). In ART, conscious perception is a consequence of a resonant state. Resonance emerges in a recurrent cortical circuit when a bottom-up spatial pattern agrees with the top-down expectation. When they do not agree, a special control mechanism is activated that resets the network and clears off erroneous expectation, thus allowing the bottom-up activity to always dominate in perception. We developed a color ART circuit and evaluated its behavior in computer simulations. The model helps to explain how traces of erroneous expectations about incoming color are eventually removed from the color perception, although their transient effect may be visible in behavioral responses or in brain imaging. Our results suggest that the color ART circuit, as a predictive computational system, is almost never penetrable, because it is equipped with computational mechanisms designed to constrain the impact of the top-down predictions on ongoing perceptual processing.

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

This article unifies neural modeling results that illustrate several basic design principles and mechanisms that are used by advanced brains to develop cortical maps with multiple psychological functions. One principle concerns how brains use a strip map that simultaneously enables one feature to be represented throughout its extent, as well as an ordered array of another feature at different positions of the strip. Strip maps include circuits to represent ocular dominance and orientation columns, place-value numbers, auditory streams, speaker-normalized speech, and cognitive working memories that can code repeated items. A second principle concerns how feature detectors for multiple functions develop in topographic maps, including maps for optic flow navigation, reinforcement learning, motion perception, and category learning at multiple organizational levels. A third principle concerns how brains exploit a spatial gradient of cells that respond at an ordered sequence of different rates. Such a rate gradient is found along the dorsoventral axis of the entorhinal cortex, whose lateral branch controls the development of time cells, and whose medial branch controls the development of grid cells. Populations of time cells can be used to learn how to adaptively time behaviors for which a time interval of hundreds of milliseconds, or several seconds, must be bridged, as occurs during trace conditioning. Populations of grid cells can be used to learn hippocampal place cells that represent the large spaces in which animals navigate. A fourth principle concerns how and why all neocortical circuits are organized into layers, and how functionally distinct columns develop in these circuits to enable map development. A final principle concerns the role of Adaptive Resonance Theory top-down matching and attentional circuits in the dynamic stabilization of early development and adult learning. Cortical maps are modeled in visual, auditory, temporal, parietal, prefrontal, entorhinal, and hippocampal cortices.

Self-organizing neural networks for universal learning and multimodal memory encoding

Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models may be used for representing various types of memories, notably episodic memory, semantic memory and procedural memory. In accordance with the notion of embodied intelligence, such neural models thus provide a computational account of how an autonomous agent may learn and adapt in a real-world environment. The efficacy of fusion ART in learning and memory shall be discussed through various examples and illustrative case studies.

[Changes in walking in the elderly]

The article addresses gait disturbances in the elderly. It emphasizes that the system that maintains the balance in resting conditions and gait is based on the hierarchical principle and its function depends on the maintenance of integration between vestibular, visual and somatosensory information as well as on cognitive functions. Walking depends on the integrity of frontal-subcortical neuronal circles that support regulatory functions. The main pathogenetic mechanisms of age-related disturbances of balance and gait are a decrease in the efficacy of spinal motorneurons activation caused by Ia-afferentation, a decrease in cortical activation and excitability of corticospinal pathways and in the intensity of intracortical inhibition. The causes of age-related changes in walking are not confined to a single system (e.g., one sensory modality) but have a multisystem character and are involved in many structures. The author analyses the results of recent studies that use functional neuroimaging methods.

Species-Specific 5 mC and 5 hmC Genomic Landscapes Indicate Epigenetic Contribution to Human Brain Evolution

Human evolution from non-human primates has seen substantial change in the central nervous system, with the molecular mechanisms underlying human brain evolution remaining largely unknown. Methylation of cytosine at the fifth carbon (5-methylcytosine; 5 mC) is an essential epigenetic mark linked to neurodevelopment, as well as neurological disease. The emergence of another modified form of cytosine (5-hydroxymethylcytosine; 5 hmC) that is enriched in the brain further substantiates a role for these epigenetic marks in neurodevelopment, yet little is known about the evolutionary importance of these marks in brain development. Here, human and monkey brain tissue were profiled, identifying 5,516 and 4,070 loci that were differentially methylated and hydroxymethylated, respectively, between the species. Annotation of these loci to the human genome revealed genes critical for the development of the nervous system and that are associated with intelligence and higher cognitive functioning, such as RELN and GNAS. Moreover, ontological analyses of these differentially methylated and hydroxymethylated genes revealed a significant enrichment of neuronal/immunological-related processes, including neurogenesis and axon development. Finally, the sequences flanking the differentially methylated/hydroxymethylated loci contained a significant enrichment of binding sites for neurodevelopmentally important transcription factors (e.g., OTX1 and PITX1), suggesting that DNA methylation may regulate gene expression by mediating transcription factor binding on these transcripts. Together, these data support dynamic species-specific epigenetic contributions in the evolution and development of the human brain from non-human primates.

Acetylcholine Neuromodulation in Normal and Abnormal Learning and Memory: Vigilance Control in Waking, Sleep, Autism, Amnesia and Alzheimer's Disease

Adaptive Resonance Theory, or ART, is a neural model that explains how normal and abnormal brains may learn to categorize and recognize objects and events in a changing world, and how these learned categories may be remembered for a long time. This article uses ART to propose and unify the explanation of diverse data about normal and abnormal modulation of learning and memory by acetylcholine (ACh). In ART, vigilance control determines whether learned categories will be general and abstract, or specific and concrete. ART models how vigilance may be regulated by ACh release in layer 5 neocortical cells by influencing after-hyperpolarization (AHP) currents. This phasic ACh release is mediated by cells in the nucleus basalis (NB) of Meynert that are activated by unexpected events. The article additionally discusses data about ACh-mediated tonic control of vigilance. ART proposes that there are often dynamic breakdowns of tonic control in mental disorders such as autism, where vigilance remains high, and medial temporal amnesia, where vigilance remains low. Tonic control also occurs during sleep-wake cycles. Properties of Up and Down states during slow wave sleep arise in ACh-modulated laminar cortical ART circuits that carry out processes in awake individuals of contrast normalization, attentional modulation, decision-making, activity-dependent habituation, and mismatch-mediated reset. These slow wave sleep circuits interact with circuits that control circadian rhythms and memory consolidation. Tonic control properties also clarify how Alzheimer's disease symptoms follow from a massive structural degeneration that includes undermining vigilance control by ACh in cortical layers 3 and 5. Sleep disruptions before and during Alzheimer's disease, and how they contribute to a vicious cycle of plaque formation in layers 3 and 5, are also clarified from this perspective.

The Striosome and Matrix Compartments of the Striatum: A Path through the Labyrinth from Neurochemistry toward Function

The striatum is a heterogeneous structure with a diverse range of neuron types and neuromodulators. Three decades of anatomical and biochemical studies have established that the neurochemical organization of striatum is not uniformly heterogeneous, but rather, can be differentiated into neurochemically discrete compartments known as striosomes (also known as patches) and matrix. These compartments are well understood to differ in their expression of neurochemical markers, with some differences in afferent and efferent connectivity and have also been suggested to have different involvement in a range of neurological diseases. However, the functional outcomes of striosome-matrix organization are poorly understood. Now, recent findings and new experimental tools are beginning to reveal that the distinctions between striosomes and matrix have distinct consequences for striatal synapse function. Here, we review recent findings that suggest there can be distinct regulation of neural function in striosome versus matrix compartments, particularly compartment-specific neurochemical interactions. We highlight that new transgenic and viral tools are becoming available that should now accelerate the pace of advances in understanding of these long-mysterious striatal compartments.

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitive-emotional learning? Why do lesions of thalamus, amygdala, hippocampus, and cortex have differential effects depending on the phase of learning when they occur? In particular, why is the hippocampus typically needed for trace conditioning, but not delay conditioning, and what do the exceptions reveal? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later do not? Why do thalamic or sensory cortical lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions during trace conditioning experiments degrade recent but not temporally remote learning? Why do orbitofrontal cortical lesions degrade temporally remote but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of prefrontal cortex after memory consolidation? How are attention and consciousness linked during conditioning? How do neurotrophins, notably brain-derived neurotrophic factor (BDNF), influence memory formation and consolidation? Is there a common output path for learned performance? A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.

Towards solving the hard problem of consciousness: The varieties of brain resonances and the conscious experiences that they support

The hard problem of consciousness is the problem of explaining how we experience qualia or phenomenal experiences, such as seeing, hearing, and feeling, and knowing what they are. To solve this problem, a theory of consciousness needs to link brain to mind by modeling how emergent properties of several brain mechanisms interacting together embody detailed properties of individual conscious psychological experiences. This article summarizes evidence that Adaptive Resonance Theory, or ART, accomplishes this goal. ART is a cognitive and neural theory of how advanced brains autonomously learn to attend, recognize, and predict objects and events in a changing world. ART has predicted that "all conscious states are resonant states" as part of its specification of mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony. It hereby provides functional and mechanistic explanations of data ranging from individual spikes and their synchronization to the dynamics of conscious perceptual, cognitive, and cognitive-emotional experiences. ART has reached sufficient maturity to begin classifying the brain resonances that support conscious experiences of seeing, hearing, feeling, and knowing. Psychological and neurobiological data in both normal individuals and clinical patients are clarified by this classification. This analysis also explains why not all resonances become conscious, and why not all brain dynamics are resonant. The global organization of the brain into computationally complementary cortical processing streams (complementary computing), and the organization of the cerebral cortex into characteristic layers of cells (laminar computing), figure prominently in these explanations of conscious and unconscious processes. Alternative models of consciousness are also discussed.

From Grouping to Coupling: A New Perceptual Organization in Vision, Psychology, and Biology

In this work, perceptual organization has been studied with the same spirit and phenomenological methods used by Gestalt psychologists. This was accomplished through new conditions that cannot be explained in terms of the classical principles of grouping. Perceptual grouping represents the way through which our visual system builds integrated elements on the basis of the maximal homogeneity among the components of the stimulus pattern. Our results demonstrated the inconsistency of organization by grouping, and more importantly, the inconsistency of the principle of similarity. On the contrary, they suggested the unique role played by the principle of dissimilarity among elements that behaves like an accent or a visual emphasis within a whole. The principle of accentuation was here considered as imparting a directional structure to the elements and to the whole object thus creating new phenomena. The salience of the resulting phenomena reveals the supremacy of dissimilarity in relation to similarity and the fact that it belongs to a further organization dynamics that we called “coupling.” In biology, coupling and its principle of accentuation are very strongly related to disruptive camouflage. Moreover, they are source of sexual attraction. They advertise the presence and elicit species identification/communication. In human beings accentuation is needed to show ourselves to others, to understand the way we dress, choose, and create clothes or invent fashion, the way we change our body accentuating several parts and hiding some others, the way we use maquillage. The existence of maquillage itself is derived from the need to accentuate something with the purpose to increase sexual attraction, to exhibit physical strength and beauty, to show or hide social status (e.g., being the king, a warrior, a priest, etc.). Last but not least, accentuation plays a basic role also in making it easier or difficult to read and understand written words.

Visuo-motor integration in unresponsive wakefulness syndrome: A piece of the puzzle towards consciousness detection?

PURPOSE

The unresponsive wakefulness syndrome (UWS) is characterized by either a profound unawareness or an impairment of large-scale cortico/subcortical connectivity. Nevertheless, some individuals with UWS could show residual markers of consciousness and cognition. In this study, we applied an electrophysiological approach aimed to identify the residual visuomotor connectivity patterns that are thought to be linked to awareness, in patients with chronic disorder of consciousness (DOC).

METHODS

We measured some markers of visuomotor and premotor-motor integration in 14 patients affected by DOC, before and after the application of transcranial direct current stimulation, delivered over the dorsolateral prefrontal cortex and the parieto-occipital area, paired to transorbital alterning current stimulation.

RESULTS

Our protocol induced a potentiation of the electrophysiological markers of visuomotor and premotor-motor connectivity, paired to a clinical improvement, in all of the patients with minimally conscious state and in one individual affected by UWS.

CONCLUSIONS

Our protocol could be a promising approach to potentiate the functional connectivity within large-scale visuomotor networks, thus allowing identifying the patients suffering from a functional locked-in syndrome (i.e. individuals showing an extreme behavioral motor dysfunction although with somehow preserved cognitive functions that can be identified only through para-clinical tests) within individuals with UWS.

Cortical Dynamics of Figure-Ground Separation in Response to 2D Pictures and 3D Scenes: How V2 Combines Border Ownership, Stereoscopic Cues, and Gestalt Grouping Rules

The FACADE model, and its laminar cortical realization and extension in the 3D LAMINART model, have explained, simulated, and predicted many perceptual and neurobiological data about how the visual cortex carries out 3D vision and figure-ground perception, and how these cortical mechanisms enable 2D pictures to generate 3D percepts of occluding and occluded objects. In particular, these models have proposed how border ownership occurs, but have not yet explicitly explained the correlation between multiple properties of border ownership neurons in cortical area V2 that were reported in a remarkable series of neurophysiological experiments by von der Heydt and his colleagues; namely, border ownership, contrast preference, binocular stereoscopic information, selectivity for side-of-figure, Gestalt rules, and strength of attentional modulation, as well as the time course during which such properties arise. This article shows how, by combining 3D LAMINART properties that were discovered in two parallel streams of research, a unified explanation of these properties emerges. This explanation proposes, moreover, how these properties contribute to the generation of consciously seen 3D surfaces. The first research stream models how processes like 3D boundary grouping and surface filling-in interact in multiple stages within and between the V1 interblob—V2 interstripe—V4 cortical stream and the V1 blob—V2 thin stripe—V4 cortical stream, respectively. Of particular importance for understanding figure-ground separation is how these cortical interactions convert computationally complementary boundary and surface mechanisms into a consistent conscious percept, including the critical use of surface contour feedback signals from surface representations in V2 thin stripes to boundary representations in V2 interstripes. Remarkably, key figure-ground properties emerge from these feedback interactions. The second research stream shows how cells that compute absolute disparity in cortical area V1 are transformed into cells that compute relative disparity in cortical area V2. Relative disparity is a more invariant measure of an object's depth and 3D shape, and is sensitive to figure-ground properties.

Resonant Cholinergic Dynamics in Cognitive and Motor Decision-Making: Attention, Category Learning, and Choice in Neocortex, Superior Colliculus, and Optic Tectum

Freely behaving organisms need to rapidly calibrate their perceptual, cognitive, and motor decisions based on continuously changing environmental conditions. These plastic changes include sharpening or broadening of cognitive and motor attention and learning to match the behavioral demands that are imposed by changing environmental statistics. This article proposes that a shared circuit design for such flexible decision-making is used in specific cognitive and motor circuits, and that both types of circuits use acetylcholine to modulate choice selectivity. Such task-sensitive control is proposed to control thalamocortical choice of the critical features that are cognitively attended and that are incorporated through learning into prototypes of visual recognition categories. A cholinergically-modulated process of vigilance control determines if a recognition category and its attended features are abstract (low vigilance) or concrete (high vigilance). Homologous neural mechanisms of cholinergic modulation are proposed to focus attention and learn a multimodal map within the deeper layers of superior colliculus. This map enables visual, auditory, and planned movement commands to compete for attention, leading to selection of a winning position that controls where the next saccadic eye movement will go. Such map learning may be viewed as a kind of attentive motor category learning. The article hereby explicates a link between attention, learning, and cholinergic modulation during decision making within both cognitive and motor systems. Homologs between the mammalian superior colliculus and the avian optic tectum lead to predictions about how multimodal map learning may occur in the mammalian and avian brain and how such learning may be modulated by acetycholine.

Laminar Neural Field Model of Laterally Propagating Waves of Orientation Selectivity

We construct a laminar neural-field model of primary visual cortex (V1) consisting of a superficial layer of neurons that encode the spatial location and orientation of a local visual stimulus coupled to a deep layer of neurons that only encode spatial location. The spatially-structured connections in the deep layer support the propagation of a traveling front, which then drives propagating orientation-dependent activity in the superficial layer. Using a combination of mathematical analysis and numerical simulations, we establish that the existence of a coherent orientation-selective wave relies on the presence of weak, long-range connections in the superficial layer that couple cells of similar orientation preference. Moreover, the wave persists in the presence of feedback from the superficial layer to the deep layer. Our results are consistent with recent experimental studies that indicate that deep and superficial layers work in tandem to determine the patterns of cortical activity observed in vivo.

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.

[On the question of the organization of brain function: cortical associations, «disconnection» syndrome and higher brain functions]

The review considers the structural/functional brain organization, the disturbance of which is accompanied by the development of cognitive and behavioral disorders. The significance of the disruption of parallel circuits connecting frontal lobes with subcortical structures (the basal ganglia, thalamus, cerebellum) is highlighted. This disruption is clinically described as "disconnection" syndrome. The associations between the basal ganglia and the cortex of the large cerebral hemispheres responsible for motor, cognitive and emotional/behavioral functions do not restricted to these spheres and is characteristic not only of frontal brain areas. There are circuits connecting other brain compartments and the basal ganglia that provide perception, and are involved in decision making on the basis of input information of different modalities.The improvement of understanding of the pathophysiology and neurochemistry of these structures opens new possibilities for selective action on some or other circuit to achieve the best therapeutic result.

ART, cognitive science, and technology transfer

Three computational examples illustrate how cognitive science can introduce new approaches to the analysis of large datasets. The first example addresses the question: how can a neural system learning from one example at a time absorb information that is inconsistent but correct, as when a family pet is called Spot and dog and animal, while rejecting similar incorrect information, as when the same pet is called wolf? How does this system transform such scattered information into the knowledge that dogs are animals, but not conversely? The second example asks: how can a real-time system, initially trained with a few labeled examples and a limited feature set, continue to learn from experience when confronted with oceans of additional information, without eroding reliable early memories? How can such individual systems adapt to their unique application contexts? The third example asks: how can a neural system that has made an error refocus attention on environmental features that it had initially ignored? Three models that address these questions, each based on the distributed adaptive resonance theory (dART) neural network, are applied to a spatial testbed created from multimodal remotely sensed data. The article summarizes key design elements of ART models, and provides links to open-source code for each system and the testbed dataset. WIREs Cogn Sci 2013, 4:707-719. doi: 10.1002/wcs.1260 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.

Neural dynamics of saccadic and smooth pursuit eye movement coordination during visual tracking of unpredictably moving targets

How does the brain coordinate saccadic and smooth pursuit eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets, and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? The neural model proposed here answers these questions. Modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF, LIP, and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model's ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about coordinated saccade-pursuit tracking.

Self-organizing ARTMAP rule discovery

The self-organizing ARTMAP rule discovery (SOARD) system derives relationships among recognition classes during online learning. SOARD training on input/output pairs produces the basic competence of direct recognition of individual class labels for new test inputs. As a typical supervised system, it learns many-to-one maps, which recognize different inputs (Spot, Rex) as belonging to one class (dog). As an ARTMAP system, it also learns one-to-many maps, allowing a given input (Spot) to learn a new class (animal) without forgetting its previously learned output (dog), even as it corrects erroneous predictions (cat). As it learns individual input/output class predictions, SOARD employs distributed code representations that support online rule discovery. When the input Spot activates the classes dogand animal, confidence in the rule dog→animal begins to grow. When other inputs simultaneously activate classes cat and animal, confidence in the converse rule, animal→dog, decreases. Confidence in a self-organized rule is encoded as the weight in a path from one class node to the other. An experience-based mechanism modulates the rate of rule learning, to keep inaccurate predictions from creating false rules during early learning. Rules may be excitatory or inhibitory so that rule-based activation can add missing classes and remove incorrect ones. SOARD rule activation also enables inputs to learn to make direct predictions of output classes that they have never experienced during supervised training. When input Rex activates its learned class dog, the rule dog→animal indirectly activates the output class animal. The newly activated class serves as a teaching signal which allows input Rex to learn direct activation of the output class animal. Simulations using small-scale and large-scale datasets demonstrate functional properties of the SOARD system in both spatial and time-series domains.

After-hyperpolarization currents and acetylcholine control sigmoid transfer functions in a spiking cortical model

Recurrent networks are ubiquitous in the brain, where they enable a diverse set of transformations during perception, cognition, emotion, and action. It has been known since the 1970's how, in rate-based recurrent on-center off-surround networks, the choice of feedback signal function can control the transformation of input patterns into activity patterns that are stored in short term memory. A sigmoid signal function may, in particular, control a quenching threshold below which inputs are suppressed as noise and above which they may be contrast enhanced before the resulting activity pattern is stored. The threshold and slope of the sigmoid signal function determine the degree of noise suppression and of contrast enhancement. This article analyses how sigmoid signal functions and their shape may be determined in biophysically realistic spiking neurons. Combinations of fast, medium, and slow after-hyperpolarization (AHP) currents, and their modulation by acetylcholine (ACh), can control sigmoid signal threshold and slope. Instead of a simple gain in excitability that was previously attributed to ACh, cholinergic modulation may cause translation of the sigmoid threshold. This property clarifies how activation of ACh by basal forebrain circuits, notably the nucleus basalis of Meynert, may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract information, as predicted by Adaptive Resonance Theory.

On the road to invariant recognition: explaining tradeoff and morph properties of cells in inferotemporal cortex using multiple-scale task-sensitive attentive learning

Visual object recognition is an essential accomplishment of advanced brains. Object recognition needs to be tolerant, or invariant, with respect to changes in object position, size, and view. In monkeys and humans, a key area for recognition is the anterior inferotemporal cortex (ITa). Recent neurophysiological data show that ITa cells with high object selectivity often have low position tolerance. We propose a neural model whose cells learn to simulate this tradeoff, as well as ITa responses to image morphs, while explaining how invariant recognition properties may arise in stages due to processes across multiple cortical areas. These processes include the cortical magnification factor, multiple receptive field sizes, and top-down attentive matching and learning properties that may be tuned by task requirements to attend to either concrete or abstract visual features with different levels of vigilance. The model predicts that data from the tradeoff and image morph tasks emerge from different levels of vigilance in the animals performing them. This result illustrates how different vigilance requirements of a task may change the course of category learning, notably the critical features that are attended and incorporated into learned category prototypes. The model outlines a path for developing an animal model of how defective vigilance control can lead to symptoms of various mental disorders, such as autism and amnesia.

How does the brain rapidly learn and reorganize view-invariant and position-invariant object representations in the inferotemporal cortex?

All primates depend for their survival on being able to rapidly learn about and recognize objects. Objects may be visually detected at multiple positions, sizes, and viewpoints. How does the brain rapidly learn and recognize objects while scanning a scene with eye movements, without causing a combinatorial explosion in the number of cells that are needed? How does the brain avoid the problem of erroneously classifying parts of different objects together at the same or different positions in a visual scene? In monkeys and humans, a key area for such invariant object category learning and recognition is the inferotemporal cortex (IT). A neural model is proposed to explain how spatial and object attention coordinate the ability of IT to learn invariant category representations of objects that are seen at multiple positions, sizes, and viewpoints. The model clarifies how interactions within a hierarchy of processing stages in the visual brain accomplish this. These stages include the retina, lateral geniculate nucleus, and cortical areas V1, V2, V4, and IT in the brain's What cortical stream, as they interact with spatial attention processes within the parietal cortex of the Where cortical stream. The model builds upon the ARTSCAN model, which proposed how view-invariant object representations are generated. The positional ARTSCAN (pARTSCAN) model proposes how the following additional processes in the What cortical processing stream also enable position-invariant object representations to be learned: IT cells with persistent activity, and a combination of normalizing object category competition and a view-to-object learning law which together ensure that unambiguous views have a larger effect on object recognition than ambiguous views. The model explains how such invariant learning can be fooled when monkeys, or other primates, are presented with an object that is swapped with another object during eye movements to foveate the original object. The swapping procedure is predicted to prevent the reset of spatial attention, which would otherwise keep the representations of multiple objects from being combined by learning. Li and DiCarlo (2008) have presented neurophysiological data from monkeys showing how unsupervised natural experience in a target swapping experiment can rapidly alter object representations in IT. The model quantitatively simulates the swapping data by showing how the swapping procedure fools the spatial attention mechanism. More generally, the model provides a unifying framework, and testable predictions in both monkeys and humans, for understanding object learning data using neurophysiological methods in monkeys, and spatial attention, episodic learning, and memory retrieval data using functional imaging methods in humans.

On the road to invariant object recognition: how cortical area V2 transforms absolute to relative disparity during 3D vision

Invariant recognition of objects depends on a hierarchy of cortical stages that build invariance gradually. Binocular disparity computations are a key part of this transformation. Cortical area V1 computes absolute disparity, which is the horizontal difference in retinal location of an image in the left and right foveas. Many cells in cortical area V2 compute relative disparity, which is the difference in absolute disparity of two visible features. Relative, but not absolute, disparity is invariant under both a disparity change across a scene and vergence eye movements. A neural network model is introduced which predicts that shunting lateral inhibition of disparity-sensitive layer 4 cells in V2 causes a peak shift in cell responses that transforms absolute disparity from V1 into relative disparity in V2. This inhibitory circuit has previously been implicated in contrast gain control, divisive normalization, selection of perceptual groupings, and attentional focusing. The model hereby links relative disparity to other visual functions and thereby suggests new ways to test its mechanistic basis. Other brain circuits are reviewed wherein lateral inhibition causes a peak shift that influences behavioral responses.

A network approach to assessing cognition in disorders of consciousness

OBJECTIVE

Conventional assessments of consciousness rely on motor responses to indicate awareness. However, overt behaviors may be absent or ambiguous in patients with disorders of consciousness (DOC) resulting in underrating capacity for cognition. fMRI during a silent picture-naming task was evaluated as an indicator of command following when conventional methods are not sufficient.

METHODS

A total of 10 patients with and without conventional evidence of awareness, who met diagnostic criteria for the minimally conscious state (MCS) (n = 5), vegetative state (VS) (n = 3), emerged from MCS (EMCS) (n = 1), and locked-in syndrome (LIS) (n = 1), participated in this observational fMRI study.

RESULTS

The LIS and EMCS patients engaged a complete network of essential language-related regions during the object-naming task. The MCS and 2 of the VS patients demonstrated both complete and partial preservation of the object-naming system. Patients who engaged a complete network scored highest on the Coma Recovery Scale-Revised.

CONCLUSIONS

This study supports the view that fMRI during object naming can elicit brain activations in patients with DOC similar to those observed in healthy subjects during command following, and patients can be stratified by completeness of the engaged neural system. These results suggest that activity of the language network may serve as an indicator of high-level cognition and possibly volitional processes that cannot be discerned through conventional behavioral assessment alone.

Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action

An intimate link exists between the predictive and learning processes in the brain. Perceptual/cognitive and spatial/motor processes use complementary predictive mechanisms to learn, recognize, attend and plan about objects in the world, determine their current value, and act upon them. Recent neural models clarify these mechanisms and how they interact in cortical and subcortical brain regions. The present paper reviews and synthesizes data and models of these processes, and outlines a unified theory of predictive brain processing.

Speaker normalization using cortical strip maps: a neural model for steady-state vowel categorization

Auditory signals of speech are speaker dependent, but representations of language meaning are speaker independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by adaptive resonance theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [Peterson, G. E., and Barney, H.L., J. Acoust. Soc. Am. 24, 175-184 (1952).] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.

How does binocular rivalry emerge from cortical mechanisms of 3-D vision?

Under natural viewing conditions, a single depthful percept of the world is consciously seen. When dissimilar images are presented to corresponding regions of the two eyes, binocular rivalry may occur, during which the brain consciously perceives alternating percepts through time. How do the same brain mechanisms that generate a single depthful percept of the world also cause perceptual bistability, notably binocular rivalry? What properties of brain representations correspond to consciously seen percepts? A laminar cortical model of how cortical areas V1, V2, and V4 generate depthful percepts is developed to explain and quantitatively simulate binocular rivalry data. The model proposes how mechanisms of cortical development, perceptual grouping, and figure-ground perception lead to single and rivalrous percepts. Quantitative model simulations of perceptual grouping circuits demonstrate influences of contrast changes that are synchronized with switches in the dominant eye percept, gamma distribution of dominant phase durations, piecemeal percepts, and coexistence of eye-based and stimulus-based rivalry. The model as a whole also qualitatively explains data about the involvement of multiple brain regions in rivalry, the effects of object attention on switching between superimposed transparent surfaces, monocular rivalry, Marroquin patterns, the spread of suppression during binocular rivalry, binocular summation, fusion of dichoptically presented orthogonal gratings, general suppression during binocular rivalry, and pattern rivalry. These data explanations follow from model brain mechanisms that assure non-rivalrous conscious percepts.

Dopaminergic and non-dopaminergic value systems in conditioning and outcome-specific revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal VAlues Triggers Option Revaluations) neural model. MOTIVATOR describes cognitive-emotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.

The phenomenal dissociation between coloration and object-hole effects in the watercolor illusion

The watercolor illusion is a color spreading effect at long-range diffusing from a thin colored contour juxtaposed to a chromatic one of higher contrast and a object-hole effect across a large area (Pinna, 1987; Pinna et al., 2001, 2003; Pinna & Reeves, 2006). The watercolored figure appears evenly colored by an opaque light veil of chromatic tint (coloration effect), with a clear surface color property spreading from the lighter contour. At the same time, the watercolored figure manifests a strong figure-ground organization and a solid figural appearance comparable to a bas-relief illuminated from the top (object-hole effect). It appears similar to a rounded surface segregated in depth, which extends out from the flat surface. The complementary region appears as a hole or empty space. The phenomenal properties of coloration and object-hole effects raise some questions. Can the two effects be considered relatively independent? Under what conditions can a possible dissociation occur? How does the dissociation of one effect, say the coloration, influence the object-hole effect and vice versa? To answer these questions two new effects related to the watercolor illusion were psychophysically studied: (1) the "uneven watercolor," based on a modified watercolor figure without volumetric and three-dimensional properties but with a strong coloration effect and (2) the "watercolor surface capture," where oblique lines within a watercolor figure appear bulging, curved in depth and segregated from those that are perceived as placed in the background or perceived through holes. The results of two experiments suggest that the coloration effect can be dissociated from the object-hole one. These results are discussed in the light of a simple summation hypothesis of the underlying effects composing the whole figurality. This hypothesis can suggest further investigation both in the phenomenal and in the neurophysiological domain.

Temporal dynamics of decision-making during motion perception in the visual cortex

How does the brain make decisions? Speed and accuracy of perceptual decisions covary with certainty in the input, and correlate with the rate of evidence accumulation in parietal and frontal cortical "decision neurons". A biophysically realistic model of interactions within and between Retina/LGN and cortical areas V1, MT, MST, and LIP, gated by basal ganglia, simulates dynamic properties of decision-making in response to ambiguous visual motion stimuli used by Newsome, Shadlen, and colleagues in their neurophysiological experiments. The model clarifies how brain circuits that solve the aperture problem interact with a recurrent competitive network with self-normalizing choice properties to carry out probabilistic decisions in real time. Some scientists claim that perception and decision-making can be described using Bayesian inference or related general statistical ideas, that estimate the optimal interpretation of the stimulus given priors and likelihoods. However, such concepts do not propose the neocortical mechanisms that enable perception, and make decisions. The present model explains behavioral and neurophysiological decision-making data without an appeal to Bayesian concepts and, unlike other existing models of these data, generates perceptual representations and choice dynamics in response to the experimental visual stimuli. Quantitative model simulations include the time course of LIP neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks. Model MT/MST interactions compute the global direction of random dot motion stimuli, while model LIP computes the stochastic perceptual decision that leads to a saccadic eye movement.

Spikes, synchrony, and attentive learning by laminar thalamocortical circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single-cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current source densities and local field potentials; and single-cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, learning, and consciousness. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

Texture segregation by visual cortex: Perceptual grouping, attention, and learning

A neural model called dARTEX is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model unifies five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits the Ben-Shahar and Zucker [Ben-Shahar, O. & Zucker, S. (2004). Sensitivity to curvatures in orientation-based texture segmentation. Vision Research, 44, 257-277] human psychophysical data on orientation-based textures. Surface-based attentional shrouds improve texture learning and classification: Brodatz texture classification rate varies from 95.1% to 98.6% with correct attention, and from 74.1% to 75.5% without attention. Object boundary output of the model in response to photographic images is compared to computer vision algorithms and human segmentations.

Consciousness CLEARS the mind

A full understanding of consciousness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? Adaptive Resonance Theory (ART) predicted a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance and Synchrony (CLEARS), including the prediction that "all conscious states are resonant states". This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. The present article reviews theoretical considerations that predicted these functional links, how they work, and some of the rapidly growing body of behavioral and brain data that have provided support for these predictions. The article also summarizes ART models that predict functional roles for identified cells in laminar thalamocortical circuits, including the six layered neocortical circuits and their interactions with specific primary and higher-order specific thalamic nuclei and nonspecific nuclei. These predictions include explanations of how slow perceptual learning can occur without conscious awareness, and why oscillation frequencies in the lower layers of neocortex are sometimes slower beta oscillations, rather than the higher-frequency gamma oscillations that occur more frequently in superficial cortical layers. ART traces these properties to the existence of intracortical feedback loops, and to reset mechanisms whereby thalamocortical mismatches use circuits such as the one from specific thalamic nuclei to nonspecific thalamic nuclei and then to layer 4 of neocortical areas via layers 1-to-5-to-6-to-4.

The role of perceptual learning on modality-specific visual attentional effects

Morrone et al. [Morrone, M. C., Denti, V., & Spinelli, D. (2002). Color and luminance contrasts attract independent attention. Current Biology, 12, 1134-1137] reported that the detrimental effect on contrast discrimination thresholds of performing a concomitant task is modality specific: performing a secondary luminance task has no effect on colour contrast thresholds, and vice versa. Here we confirm this result with a novel task involving learning of spatial position, and go on to show that it is not specific to the cardinal colour axes: secondary tasks with red-green stimuli impede performance on a blue-yellow task and vice versa. We further show that the attentional effect can be abolished with continued training over 2-4 training days (2-20 training sessions), and that the effect of learning is transferable to new target positions. Given the finding of transference, we discuss the possibility that V4 is a site of plasticity for both stimulus types, and that the separation is due to a luminance-colour separation within this cortical area.

Computational aspects of feedback in neural circuits

It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning) to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks.

Circuits of neurons in the brain have an abundance of feedback connections, both on the level of local microcircuits and on the level of synaptic connections between brain areas. But the functional role of these feedback connections is largely unknown. We present a computational theory that characterizes the gain in computational power that feedback can provide in such circuits. It shows that feedback endows standard models for neural circuits with the capability to emulate arbitrary Turing machines. In fact, with suitable feedback they can simulate any dynamical system, in particular any conceivable analog computer. Under realistic noise conditions, the computational power of these circuits is necessarily reduced. But we demonstrate through computer simulations that feedback also provides a significant gain in computational power for quite detailed models of cortical microcircuits with in vivo–like high levels of noise. In particular it enables generic cortical microcircuits to carry out computations that combine information from working memory and persistent internal states in real time with new information from online input streams.