What is the neural representation of a speech code as it evolves in time? A neural model simulates data concerning segregation and integration of phonetic percepts. Hearing two phonetically related stops in a VC-CV pair (V = vowel; C = consonant) requires 150 ms more closure time than hearing two phonetically different stops in a VC,-C2V pair. Closure time also varies with long-term stimulus rate. The model simulates rate-dependent category boundaries that emerge from feedback: 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 code is a resonant wave. It emerges after bottom-up signals from the working memory select list chunks which read out top-down expectations that amplify and focus attention on consistent working memory items. In VCi-C2V pairs, resonance is reset by mismatch of Cj with the C, expectation. In VC-CV pairs, resonance prolongs a repeated C. What is the nature of the process that converts brain events into behavioral percepts? An answer to this question is needed in order to understand how the brain controls behavior and how the brain is, in turn, shaped by environmental feedback that is experienced on the behavioral level. The nature of this connection also needs to be understood in order to develop neurally plausible connectionist models. Without it, a correct linking hypothesis cannot be developed between psychological data and the brain mechanisms from which they are generated.

Some recent formal models of Pavlovian and instrumental conditioning contain internal paradoxes that restrict their predictive power. These paradoxes can be traced to an inadequate formulation of how mechanisms of short-term memory and long-term memory work together to control the shifting balance between the processing of expected and unexpected events. Once this formulation is strengthened, a unified processing framework is suggested wherein attentional and orienting subsystems coexist in a complementary relationship that controls the adaptive self-organization of internal representations in response to expected and unexpected events. In this framework, conditioning and attentional constructs can be more directly validated by interdisciplinary paradigms in which seemingly disparate phenomena can be shown to share similar physiological and pharmacological mechanisms. A model of cholinergic-catecholaminergic interactions suggests how drive, reinforcer, and arousal inputs regulate motivational baseline, hysteresis, and rebound, with the hippocampus as a final common path. Extinction, conditioned emotional responses, conditioned avoidance responses, secondary

Developmental psychology and developmental neuropsychology have traditionally focused on the study of children. But these two fields are also supposed to be about the study of change, i.e. changes in behavior, changes in the neural structures that underlie behavior, and changes in the relationship between mind and brain across the course of development. Ironically, there has been relatively little interest in the mechanisms responsible for change in the last 15–20 years of developmental research. The reasons for this de-emphasis on change have a great deal to do with a metaphor for mind and brain that has influenced most of experimental psychology, cognitive science and neuropsychology for the last few decades, i.e. the metaphor of the serial digital computer. We will refer to this particu-

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

How is development of cortical maps in V1 coordinated across cortical layers to form cortical columns? Previous neural models propose how maps of orientation (OR), ocular dominance (OD), and related properties develop in V1. These models show how spontaneous activity, before eye opening, combined with correlation learning and competition, can generate maps similar to those found in vivo. These models have not discussed laminar architecture or how cells develop and coordinate their connections across cortical layers. This is an important problem since anatomical evidence shows that clusters of horizontal connections form, between iso-oriented regions, in layer 2/3 before being innervated by layer 4 afferents. How are orientations in different layers aligned before these connections form? Anatomical evidence demonstrates that thalamic afferents wait in the subplate for weeks before innervating layer 4. Other evidence shows that ablation of the cortical subplate interferes with the development of OR and OD columns. The model proposes how the subplate develops OR and OD maps, which then entrain and coordinate the development of maps in other lamina. The model demonstrates how these maps may develop in layer 4 by using a known transient subplate-to-layer 4 circuit as a teacher. The model subplate also guides the early clustering of horizontal connections in layer 2/3, and the formation of the interlaminar circuitry that forms cortical columns. It is shown how layer 6 develops and helps to stabilize the network when the subplate atrophies. Finally the model clarifies how BDNF manipulations may influence cortical development.

An outstanding problem in psychiatry concerns how to link discoveries about the pharmacological, neurophysiological, and neuroanatomical substrates of mental disorders to the abnormal behaviors that they control. A related problem concerns how to understand abnormal behaviors on a continuum with normal behaviors. During the past few decades, neural models have been developed of how normal cognitive and emotional processes learn from the environment, focus attention and act upon motivationally important events, and cope with unexpected events. When arousal or volitional signals in these models are suitably altered, they give rise to symptoms that strikingly resemble negative and positive symptoms of schizophrenia, including flat affect, impoverishment of will, attentional problems, loss of a theory of mind, thought derailment, hallucinations, and delusions. The present article models how emotional centers of the brain, such as the amygdala, interact with sensory and prefrontal cortices ...

This article further develops the FACADE neural model of 3-D vision and figure-ground perception to quantitatively explain properties of the McCollough effect (ME). The model proposes that many ME data result from visual system mechanisms whose primary function is to adaptively align, through learning, boundary and surface representations that are positionally shifted due to the process of binocular fusion. For example, binocular boundary representations are shifted by binocular fusion relative to monocular surface representations, yet the boundaries must become positionally aligned with the surfaces to control binocular surface capture and filling-in. The model also includes perceptual reset mechanisms that use habituative transmitters in opponent processing circuits. Thus the model shows how ME data may arise from a combination of mechanisms that have a clear functional role in biological vision. Simulation results with a single set of parameters quantitatively fit data from 13 experiments that probe the nature of achromatic/chromatic and monocular/binocular interactions during induction of the ME. The model proposes how perceptual learning, opponent processing, and habituation at both monocular and binocular surface representations are involved, including early thalamocortical sites. In particular, it explains the anomalous ME utilizing these multiple processing sites. Alternative models of the ME are also summarized and compared with the present model. Ó 2002 Published by Elsevier Science Ltd.

What brain mechanisms underlie autism and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the iSTART model, which proposes how cognitive, emotional, timing, and motor processes that involve brain regions like prefrontal and temporal cortex, amygdala, hippocampus, and cerebellum may interact together to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes, notably a combination of underaroused emotional depression in the amygdala and related affective brain regions, learning of hyperspecific recognition categories in temporal and prefrontal cortices, and breakdowns of adaptively timed attentional and motor circuits in the hippocampal system and cerebellum. The model clarifies how malfunctions in a subset of these mechanisms can, though a system-wide vicious circle of environmentally mediated feedback, cause and maintain problems with them all. ii

We have recently introduced a neural network mobile robot controller (NETMORC) that autonomously learns the forward and inverse odometry of a differential drive robot through an unsupervised learning-by-doing cycle. After an initial learning phase, the controller can move the robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. In addition, the forward odometric map allows the robot to reach targets in the absence of sensory feedback. The controller is also able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we review the NETMORC architecture and describe its simplified algorithmic implementation, we present new, quantitative results on NETMORC's performance and adaptability under noise-free and noisy conditions, we compare NETMORC's performance on a trajectory-following task with the performance...