Neuronal Coding Of Perceptual Systems

This book provides a most complete overview of physiological and psychophysical properties of perceptual systems in man and animals. The information processing chains are described step-by-step from the stimuli of the respective environments, via the perceptual neuronal coding networks to conscious sensations and behaviour. Articles by W G K Backhaus, A G Clark, B Hiley, A Iznak, M Kavaliers, B Kramer, A Michelsen, C Neumeyer, G A Orban, T Radil, D G Stavenga, M Stengl, U Thurm, R L DeValois, R Wehner, J S Werner, W Wiltschko, and related short articles. Contents:PrefaceIntroductory LectureNeuronal Coding:Vision: Neuronal Coding of Colour, Space, Form, Motion, and Polarised Light PerceptionHearing and Touch: Neuronal Coding of Auditory and Mechano PerceptionTaste and Smell: Neuronal Coding of Chemical PerceptionNeuronal Coding of Temperature, Electro, Magneto, and Pain PerceptionNeuronal Network SimulationsInternal Representations:Neuronal Coding, Qualia, and Sensations (Consciousness)Participants Readership: Students and researchers in biophysics, neurosciences and physiology. Keywords:Neuronal Coding;Perceptual Systems;Biophysics;Biocybernetics;Physiology;Psychology

Neuronal coding of information coming from external and internal environments and transducted by sensory receptors constitutes a basic biophysical problem. After the coding phase, such information orients organism responses, shaping complex behavioural patterns. The characteristics of both neurons (interneurons with re-entering connections, latency times, filter bandwidth with respect to input signals, logic operations on multiple convergent signals) and neuron nets (reverberating nets, feedback/feed-forward connections, oscillations due to endogenous activity patterns) are important for coding mechanisms. Neuronal coding is implied also in the higher phases of information processing linked to consciousness, when neuronal activity patterns are related to perceptual mental representations.

Experimental and theoretical neuroscientists use Bayesian approaches to analyze the brain mechanisms of perception, decision-making, and motor control.

Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. The book collaborators contribute various chapters that describe results in different systems (visual, auditory, somatosensory perception, etc.) and different species (monkeys, rats, humans, etc). Concentrating on the recording and analysis of the firing of single and multiple neurons, and the analysis and recording of other integrative measures of network activity and network states—such as local field potentials or current source densities—is the basis of the introductory chapters. Provides a comprehensive and interdisciplinary approach Describes topics of interest to a wide range of researchers The book then moves forward with the description of the principles of neural coding for different functions and in different species and concludes with theoretical and modeling works describing how information processing functions are implemented. The text not only contains the most important experimental findings, but gives an overview of the main methodological aspects for studying neural coding. In addition, the book describes alternative approaches based on simulations with neural networks and in silico modeling in this highly interdisciplinary topic. It can serve as an important reference to students and professionals.

This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial & Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed & selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation & implementation, image processing & engineering applications.

Handbook of Perception, Volume VIII: Perceptual Coding covers perceptual coding of space, time, and objects, including sensory memory systems and the relations between verbal and perceptual codes. This volume contains contributions that focus on such subjects as the compound eye; the problems of the perceptual constancies and of intersensory coordination in perceptual development; the visual perception of objects in space; and perception of motion. Topics on the perception of color, the representation of temporal, auditory, and haptic perception; and the relationship between verbal and perceptual codes are discussed in detail as well. This book will be of use to psychologists, biologists, and those interested in the study of perceptual codes.

Originally published in 1973, this book deals with what were, even at that time, the well-known neural coding processes of the sensory transmission processes. The book was written to demonstrate the common features of the various senses. It concentrates on the most peripheral neural aspects of the senses starting with the physical transduction process and culminating in the arrival of signals at the brain.

A survey of probabilistic approaches to modeling and understanding brain function. Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how the brain transforms raw sensory information into a form that is useful for goal-directed behavior. A fundamental question that is seldom addressed by these studies, however, is why the brain uses the types of representations it does and what evolutionary advantage, if any, these representations confer. It is difficult to address such questions directly via animal experiments. A promising alternative is to use probabilistic principles such as maximum likelihood and Bayesian inference to derive models of brain function. This book surveys some of the current probabilistic approaches to modeling and understanding brain function. Although most of the examples focus on vision, many of the models and techniques are applicable to other modalities as well. The book presents top-down computational models as well as bottom-up neurally motivated models of brain function. The topics covered include Bayesian and information-theoretic models of perception, probabilistic theories of neural coding and spike timing, computational models of lateral and cortico-cortical feedback connections, and the development of receptive field properties from natural signals.