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.

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A long-term bias in the head-waving behavior of Aplysia can be induced using bright lights as an aversive stimulus: coupling onset of the lights with head movements to one side results in a bias away from that side (Cook & Carew, 1986). This bias has been interpreted as a form of operant conditioning, whereby the Aplysia learns the adverse effect of its own actions. A similar form of head-waving behavior was previously modeled by Raymond, Baxter, Buonomano, and Byrne (1992) using a neural oscillator. In this article we simulate the head-waving behavior using a recurrent gated dipole, a nonlinear dynamical neural model that has previously been used to explain various data including oscillatory behavior in biological pacemakers. Our model has generated quantitative fits to the experimental data of Cook and Carew (1986), and it has suggested a new experimental hypothesis on the nature of the head-waving behavior. Keywords: Gated dipole, nonassociative learning, head-waving, aplysia, opera...

Many connectionist approaches to musical expectancy and music composition let the question of "What next?" overshadow the equally important question of "When next?". One cannot escape the latter question, one of temporal structure, when considering the perception of musical meter. We view the perception of metrical structure as a dynamic process where the temporal organization of external musical events synchronizes, or entrains, a listener's internal processing mechanisms. This article introduces a novel connectionist unit, based upon a mathematical model of entrainment, capable of phase- and frequency-locking to periodic components of incoming rhythmic patterns. Networks of these units can self-organize temporally structured responses to rhythmic patterns. The resulting network behavior embodies the perception of metrical structure. The article concludes with a discussion of the implications of our approach for theories of metrical structure and musical expectancy. Connection Science...

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Frequency-selective augmenting responses by short-term synaptic depression in cat neocortex

Acknowledgements: The authors wish to thank Carol Yanakakis Je erson for her valuable assistance in the This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how achild, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of

-Grossberg, S. (1984). Some psychophysiological and pharmacological