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.

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were no...

The percepts known variously as the line motion illusion, motion induction, and transformational apparent motion have attracted a great deal of experimental interest, since they sensitively probe interactions between preattentive and attentive vision processes. The present article develops a neural model that qualitatively explains essentially all the data reported thus far, and quantitatively simulates key illustrative percepts. The model suggests how these data arise from neural mechanisms of preattentive boundary and surface formation, long-range apparent motion, formmotion interactions, and spatial attention. The boundary and surface formation processes model aspects of the interblob V1 ! interstripe V2 ! V4 and blob V1 ! thin stripe V2 ! V4 cortical processing streams, respectively. The long-range apparent motion process models aspects of the V1 ! MT ! MST processing stream. An interstream V2 ! MT form-motion interaction is proposed to allow the motion processing stream to track ...

A neural model of motion perception simulates psychophysical data concerning first-order and second-order motion stimuli, including the reversal of perceived motion direction with distance from the stimulus (\Gamma display), and data about directional judgments as a function of relative spatial phase or spatial and temporal frequency. Many other second-order motion percepts that have been ascribed to a second non-Fourier processing stream can also be explained in the model by interactions between ON and OFF cells within a single, neurobiologically interpreted magnocellular processing stream. Yet other percepts may be traced to interactions between form and motion processing streams, rather than to processing within multiple motion processing streams. The model hereby explains why monkeys with lesions of of the parvocellular layers, but not the magnocellular layers, of the lateral geniculate nucleus (LGN) are capable of detecting the correct direction of second-order motion, why most ce...

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 ...

Experimental data show that biological synapses behave quite differently from the symbolic synapses in all common artificial neural network models. Biological synapses are

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In this paper a novelty filter is introduced which allows a robot operating in an unstructured environment to produce a self-organised model of its surroundings and to detect deviations from the learned model. The environment is perceived using the robot’s 16 sonar sensors. The algorithm produces a novelty measure for each sensor scan relative to the model it has learned. This means that it highlights stimuli which have not been previously experienced. The novelty filter proposed uses a model of habituation. Habituation is a decrement in behavioural response when a stimulus is presented repeatedly. Robot experiments are presented which demonstrate the reliable operation of the filter in a number of environments. 1.

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

The use of mobile robots for inspection tasks is an attractive idea. A robot can travel through environments that humans cannot, and can be trained to identify sensor perceptions that signify potential or actual problems without requiring human intervention. However, in many cases, the appearance of a problem can vary widely, and ensuring that the robot does not miss any possible appearance of the problem (false negatives) is virtually impossible using conventional methods. This paper presents an alternative methodology using novelty detection. A neural network is trained to ignore normal perceptions that do not suggest any problems, so that anything that the robot has not sensed before is highlighted as a possible fault. This makes the incidence of false negatives less likely. We propose a novelty filter that can operate on-line, so that each new input is evaluated for novelty with respect to the data seen so far. The novelty filter learns to ignore inputs that have been sensed previously, or where similar inputs have been perceived. We demonstrate the use of the novelty filter on a series of simple inspection tasks using a mobile robot. The robot highlights those parts of an environment that are novel in some way, that is they are not part of the model acquired during exploration of a different environment. We show the effectiveness of the method using inputs from both sonar sensors and a monochrome camera.