While the ability of animals to learn rhythms is an unquestionable fact, the underlying neurophysiological mechanisms are still no more than conjectures. This monograph explores the requirements of such mechanisms, reviews those previously proposed and postulates a new one based on a direct electric coding of stimulation frequencies. Experi mental support for the option taken is provided both at the single neuron and neural network levels. More specifically, the material presented divides naturally into four parts: a description of the experimental and theoretical framework where this work becomes meaningful (Chapter 2), a detailed specifica tion of the pacemaker neuron model proposed together with its valida tion through simulation (Chapter 3), an analytic study of the behavior of this model when submitted to rhythmic stimulation (Chapter 4) and a description of the neural network model proposed for learning, together with an analysis of the simulation results obtained when varying seve ral factors related to the connectivity, the intraneuronal parameters, the initial state and the stimulation conditions (Chapter 5). This work was initiated at the Computer and Information Science Depart ment of the University of Massachusetts, Amherst, and completed at the Institut de c Lber n e t Lca of the Universitat Politecnica de Catalunya, Barcelona. Computers at the latter place have adopted Catalan as their mother tongue and thus some computer-made figures in this monograph, specially those in Chapter 5, appear labeled in that tongue.

The basic principles guiding sensing, perception and action in bio systems seem to rely on highly organised spatial-temporal dynamics. In fact, all biological senses, (visual, hearing, tactile, etc.) process signals coming from different parts distributed in space and also show a complex time evolution. As an example, mammalian retina performs a parallel representation of the visual world embodied into layers, each of which r- resents a particular detail of the scene. These results clearly state that visual perception starts at the level of the retina, and is not related uniquely to the higher brain centres. Although vision remains the most useful sense guiding usual actions, the other senses, ?rst of all hearing but also touch, become essential particularly in cluttered conditions, where visual percepts are somehow obscured by environment conditions. Ef?cient use of hearing can be learnt from acoustic perception in animals/insects, like crickets, that use this ancient sense more than all the others, to perform a vital function, like mating.

This book takes a new and up-to-date look at the prominent theory that the left hemisphere is specialised for representing patterns extended in time whereas the right hemisphere represents simultaneous or 'spatial' patterns. What makes it unique in the field is that it looks at this theory from a neurobiological basis. It suggests that the difference resides in the range of conduction times in the axons connecting different regions of the cortex in each hemisphere. This hypothesis is discussed with respect to theoretical models of brain dynamics, and both gross and microscopic structure of the hemispheres. It deals with the psychological implications of the hypothesis for higher functions of the human cerebrum and outlines testable implications wherever possible.

Connection science is a new information-processing paradigm which attempts to imitate the architecture and process of the brain, and brings together researchers from disciplines as diverse as computer science, physics, psychology, philosophy, linguistics, biology, engineering, neuroscience and AI. Work in Connectionist Natural Language Processing (CNLP) is now expanding rapidly, yet much of the work is still only available in journals, some of them quite obscure. To make this research more accessible this book brings together an important and comprehensive set of articles from the journal CONNECTION SCIENCE which represent the state of the art in Connectionist natural language processing; from speech recognition to discourse comprehension. While it is quintessentially Connectionist, it also deals with hybrid systems, and will be of interest to both theoreticians as well as computer modellers. Range of topics covered: Connectionism and Cognitive Linguistics Motion, Chomsky's Government-binding Theory Syntactic Transformations on Distributed Representations Syntactic Neural Networks A Hybrid Symbolic/Connectionist Model for Understanding of Nouns Connectionism and Determinism in a Syntactic Parser Context Free Grammar Recognition Script Recognition with Hierarchical Feature Maps Attention Mechanisms in Language Script-Based Story Processing A Connectionist Account of Similarity in Vowel Harmony Learning Distributed Representations Connectionist Language Users Representation and Recognition of Temporal Patterns A Hybrid Model of Script Generation Networks that Learn about Phonological Features Pronunciation in Text-to-Speech Systems

This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Discovering hidden recurring patterns in observable behavioral processes is an important issue frequently faced by numerous advanced students and researchers across many research areas, including psychology, biology, sports, robotics, media, finance, and medicine. As generally, themany powerful methods included in statistical software packages were not developed for this kind of analysis, discovering such patterns has proven a particularly difficult task, due to a lack of a) adequate formalized models of the kinds of patterns to look for, b) corresponding detection algorithms and c) their implementation in available software. The research described in this book is based on the application of such pattern types, algorithms and software developed from the late seventies to the present in the context of research in collaboration with human and animal behavioral research teams at internationally leading universities in the US and Europe, thus testing the usefulness and validity of the pattern types, algorithms and software in numerous research areas. With the (scale independent statistical hierarchical and fractal-like) T-Pattern at its heart, a set of proposed pattern types, called the T-System, forms the basis for the search algorithms implemented as the software THEME (TM) (vs. 6), which is easily available in free educational and full commercial versions.

We live in a complex and dynamically changing acoustic environment. To this end, the auditory cortex of humans has developed the ability to process a remarkable amount of diverse acoustic information with apparent ease. In fact, a phylogenetic comparison of auditory systems reveals that human auditory association cortex in particular has undergone extensive changes relative to that of other species, although our knowledge of this remains incomplete. In contrast to other senses, human auditory cortex receives input that is highly pre-processed in a number of sub-cortical structures; this suggests that even primary auditory cortex already performs quite complex analyses. At the same time, much of the functional role of the various sub-areas in human auditory cortex is still relatively unknown, and a more sophisticated understanding is only now emerging through the use of contemporary electrophysiological and neuroimaging techniques. The integration of results across the various techniques signify a new era in our knowledge of how human auditory cortex forms basis for auditory experience. This volume on human auditory cortex will have two major parts. In Part A, the principal methodologies currently used to investigate human auditory cortex will be discussed. Each chapter will first outline how the methodology is used in auditory neuroscience, highlighting the challenges of obtaining data from human auditory cortex; second, each methods chapter will provide two or (at most) three brief examples of how it has been used to generate a major result about auditory processing. In Part B, the central questions for auditory processing in human auditory cortex are covered. Each chapter can draw on all the methods introduced in Part A but will focus on a major computational challenge the system has to solve. This volume will constitute an important contemporary reference work on human auditory cortex. Arguably, this will be the first and most focused book on this critical neurological structure. The combination of different methodological and experimental approaches as well as a diverse range of aspects of human auditory perception ensures that this volume will inspire novel insights and spurn future research.