The full power of combining experiment and theory has yet to be unleashed on studies of the neural mechanisms in the brain involved in acoustic information processing. In recent years, enormous amounts of physiological data have been generated in many laboratories around the world, characterizing electrical responses of neurons to a wide array of acoustic stimuli at all levels of the auditory neuroaxis. Modern approaches of cellular and molecular biology are leading to new understandings of synaptic transmission of acoustic information, while application of modern neuro-anatomical methods is giving us a fairly comprehensive view ofthe bewildering complexity of neural circuitry within and between the major nuclei of the central auditory pathways. Although there is still the need to gather more data at all levels of organization, a ma jor challenge in auditory neuroscience is to develop new frameworks within which existing and future data can be incorporated and unified, and which will guide future laboratory ex perimentation. Here the field can benefit greatly from neural modeling, which in the central auditory system is still in its infancy. Indeed, such an approach is essential if we are to address questions related to perception of complex sounds including human speech, to the many di mensions of spatial hearing, and to the mechanisms that underlie complex acoustico-motor behaviors.

Reviews and discussions of contemporary and relevant topics by leading investigators, essential for all those wishing to take advantage of the latest and greatest in this emerging field.

Understanding tinnitus and treating patients with tinnitus must involve many disciplines of basic science and clinical practice. The book provides comprehensive coverage of a wide range of topics related to tinnitus including its pathophysiology, etiology and treatment. The chapters are written by researchers and clinicians who are active in the areas of basic science such as neurophysiology and neuroanatomy and in clinical specialties of psychology, psychiatry, audiology and otolaryngology.* Comprehensive coverage of the pathology and cause of tinnitus including genetics * Hyperacusis, phonophobia and other abnormalities in perception of sounds * The role of neural plasticity in tinnitus

EEG-Based Experiment Design for Major Depressive Disorder: Machine Learning and Psychiatric Diagnosis introduces EEG-based machine learning solutions for diagnosis and assessment of treatment efficacy for a variety of conditions. With a unique combination of background and practical perspectives for the use of automated EEG methods for mental illness, it details for readers how to design a successful experiment, providing experiment designs for both clinical and behavioral applications. This book details the EEG-based functional connectivity correlates for several conditions, including depression, anxiety, and epilepsy, along with pathophysiology of depression, underlying neural circuits and detailed options for diagnosis. It is a necessary read for those interested in developing EEG methods for addressing challenges for mental illness and researchers exploring automated methods for diagnosis and objective treatment assessment. - Written to assist in neuroscience experiment design using EEG - Provides a step-by-step approach for designing clinical experiments using EEG - Includes example datasets for affected individuals and healthy controls - Lists inclusion and exclusion criteria to help identify experiment subjects - Features appendices detailing subjective tests for screening patients - Examines applications for personalized treatment decisions

Solid and transparent data analysis is the most important basis for reliable interpretation of experiments. The technique of parallel spike train recordings using multi-electrode arrangements has been available for many decades now, but only recently gained wide popularity among electro physiologists. Many traditional analysis methods are based on firing rates obtained by trial-averaging, and some of the assumptions for such procedures to work can be ignored without serious consequences. The situation is different for correlation analysis, the result of which may be considerably distorted if certain critical assumptions are violated. The focus of this book is on concepts and methods of correlation analysis (synchrony, patterns, rate covariance), combined with a solid introduction into approaches for single spike trains, which represent the basis of correlations analysis. The book also emphasizes pitfalls and potential wrong interpretations of data due to violations of critical assumptions.

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, blind source separation by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, telecommunications, medical signal-processing and several data mining issues. This book presents theories and applications of ICA and includes invaluable examples of several real-world applications. Based on theories in probabilistic models, information theory and artificial neural networks, several unsupervised learning algorithms are presented that can perform ICA. The seemingly different theories such as infomax, maximum likelihood estimation, negentropy maximization, nonlinear PCA, Bussgang algorithm and cumulant-based methods are reviewed and put in an information theoretic framework to unify several lines of ICA research. An algorithm is presented that is able to blindly separate mixed signals with sub- and super-Gaussian source distributions. The learning algorithms can be extended to filter systems, which allows the separation of voices recorded in a real environment (cocktail party problem). The ICA algorithm has been successfully applied to many biomedical signal-processing problems such as the analysis of electroencephalographic data and functional magnetic resonance imaging data. ICA applied to images results in independent image components that can be used as features in pattern classification problems such as visual lip-reading and face recognition systems. The ICA algorithm can furthermore be embedded in an expectation maximization framework for unsupervised classification. Independent Component Analysis: Theory and Applications is the first book to successfully address this fairly new and generally applicable method of blind source separation. It is essential reading for researchers and practitioners with an interest in ICA.

Featuring contributions from a stellar team of expert contributors in the areas of audiology, psychology, anatomy, neuroscience, imaging science, and epidemiology, this book addresses major controversies in the field of auditory processing and its disorders. The contributors consider a range of topics including the history of the field, contemporary anatomical models, auditory processing streams, neuroplasticity, professional models, modality specificity, music perception and its disorders, speech recognition, aging, educational outcomes, tinnitus, and auditory neuropathy.