A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how the visual cortex sees. Visual cortex, like many parts of perceptual and cognitive neocortex, is organized into six main layers of cells, as well as characteristic sub-lamina. Here it is proposed how these layered circuits help to realize processes of development, learning, perceptual grouping, attention, and 3D vision through a combination of bottom-up, horizontal, and top-down interactions. A key theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. These results thus begin to unify three fields: infant cortical development, adult cortical neurophysiology and anatomy, and adult visual perception. The identified cortical mechanisms promise to generalize to explain how other perceptual and cognitive processes work.

A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model describes how monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the LGN and cortical areas V1, V2, and V4. It proposes how interactions between layers 4, 3B, and 2/3 in V1 and V2 contribute to stereopsis, and how binocular and monocular information combine to form 3D boundary and surface representations. The model includes two main new developments: (1) It clarifies how surface-toboundary feedback from V2 thin stripes to pale stripes helps to explain data about stereopsis. This feedback has previously been used to explain data about 3D figure-ground perception. (2) It proposes that the binocular false match problem is subsumed under the Gestalt grouping problem. In particular, the disparity filter, which helps to solve the correspondence problem by eliminating false matches, is realized using inhibitory interneurons as part of the perceptual grouping process by horizontal connections in layer 2/3 of cortical area V2. The enhanced model explains all the psychophysical data previously simulated by Grossberg and Howe (2003), such as contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind

What is the neural representation of a speech code as it evolves in time? How do listeners integrate temporally distributed phonemic information across hundreds of milliseconds, even backwards in time, into coherent representations of syllables and words? What sorts of brain mechanisms encode the correct temporal order, despite such backwards effects, during speech perception? How does the brain extract rate- invariant properties of variable-rate speech? This article describes an emerging neural model that suggests answers to these questions, while quantitatively simulating challenging data about audition, speech and word recognition. This model includes bottom-up filtering, horizontal competitive, and top-down attentional interactions between a working memory for short-term storage of phonetic items and a list categorization network for grouping sequences of items. The conscious speech and word recognition code is suggested to be a resonant wave of activation across such a network, and a percept of silence is proposed to be a temporal discontinuity in the rate with which such a resonant wave evolves. Properties of these resonant waves can be traced to the brain mechanisms whereby auditory, speech, and language representations are learned in a stable way through time. Because resonances are proposed to control stable learning, the model is called an Adaptive Resonance Theory, or ART, model.

Parallel pattern recognition requires great computational resources; it is NP-complete. From an engineering point of view it is desirable to achieve good performance with limited resources. For this purpose, we develop a serial model for visual pattern recognition based on the primate selective attention mechanism. The idea in selective attention is that not all parts of an image give us information. If we can attend only to the relevant parts, we can recognize the image more quickly and using less resources. We simulate the primitive, bottom-up attentive level of the human visual system with a saliency scheme and the more complex, top-down, temporally sequential associative level with observable Markov models. In between, there is a neural network that analyses image parts and generates posterior probabilities as observations to the Markov model. We test our model first on a handwritten numeral recognition problem and then apply it to a more complex face recognition problem. Our results indicate the promise of this approach in complicated vision applications.

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What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such systems, guided and limited by associative memory, is similar to the stream of consciousness. A specific architecture of an artificial system, termed articon, is introduced that by its very design has to claim being conscious. Non-verbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the flow of inner states of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills – when conscious informa-tion processing is replaced by subconscious – is elucidated. Arguments confirming that phe-nomenal experience is a result of cognitive processes are presented. Possible philosophical objec-tions based on the Chinese room and other arguments are discussed, but they are insufficient to refute articon’s claims that it is conscious. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human.

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

How do we consciously see surfaces infused with lightness and color at the correct depths? Random Dot Stereograms (RDS) probe how binocular disparity between the two eyes can generate conscious surface percepts. Dense RDS do so despite the fact that they include multiple false binocular matches. Sparse stereograms do so across large contrast-free regions with no binocular matches. Stereograms that define occluding and occluded surfaces lead to surface percepts wherein partially occluded textured surfaces are completed behind occluding textured surfaces at a spatial scale much larger than that of the texture elements themselves. Earlier models suggest how the brain detects binocular disparity, but not how RDS generate conscious percepts of 3D surfaces. This article proposes a neural model that predicts and simulates how the layered circuits of visual cortex generate 3D surface percepts using interactions between boundary and surface representations that obey complementary computational rules. The model clarifies how interactions between layers 4, 3B, and 2/3A in V1 and V2 contribute to stereopsis, and proposes how 3D perceptual grouping laws in V2 interact with 3D surface filling-in operations in V1, V2, and V4 to generate 3D surface percepts in which figures are separated from their backgrounds.

A quantum dynamic model of decision-making is presented, and it is compared with a previously established Markov model. Both the quantum and the Markov models are formulated as random walk decision processes, but the probabilistic principles differ between the two approaches. Quantum dynamics describe the evolution of complex valued probability amplitudes over time, whereas Markov models describe the evolution of real valued probabilities over time. Quantum dynamics generate interference effects, which are not possible with Markov models. An interference effect occurs when the probability of the union of two possible paths is smaller than each individual path alone. The choice probabilities and distribution of choice response time for the quantum model are derived, and the predictions are contrasted with the Markov model.

Abstract — Creative thinking is one of the hallmarks of human-level competence. Although it is still a poorly understood subject speculative ideas about brain processes involved in creative thinking may be implemented in computational models. A review of different approaches to creativity, insight and intuition is presented. Two factors are essential for creativity: imagination and selection or filtering. Imagination should be constrained by experience, while filtering in the case of creative use of words may be based on semantic and phonological associations. Analysis of brain processes involved in invention of new words leads to practical algorithms that create many interesting and novel names associated with a set of keywords. I.