Annotation As one of our highest expressions of thought and creativity, music has always been a difficult realm to capture, model, and understand. The connectionist paradigm, now beginning to provide insights into many realms of human behavior, offers a new and unified viewpoint from which to investigate the subtleties of musical experience. Music and Connectionism provides a fresh approach to both fields, using the techniques of connectionism and parallel distributed processing to look at a wide range of topics in music research, from pitch perception to chord fingering to composition.The contributors, leading researchers in both music psychology and neural networks, address the challenges and opportunities of musical applications of network models. The result is a current and thorough survey of the field that advances understanding of musical phenomena encompassing perception, cognition, composition, and performance, and in methods for network design and analysis.Peter M. Todd is a doctoral candidate in the PDP Research Group of the Psychology Department at Stanford University. Gareth Loy is an award-winning composer, a lecturer in the Music Department of the University of California, San Diego, and a member of the technical staff of Frox Inc.Contributors. Jamshed J. Bharucha. Peter Desain. Mark Dolson. Robert Gjerclingen. Henkjan Honing. B. Keith Jenkins. Jacqueline Jons. Douglas H. Keefe. Tuevo Kohonen. Bernice Laden. Pauli Laine. Otto Laske. Marc Leman. J. P. Lewis. Christoph Lischka. D. Gareth Loy. Ben Miller. Michael Mozer. Samir I. Sayegh. Hajime Sano. Todd Soukup. Don Scarborough. Kalev Tiits. Peter M. Todd. Kari Torkkola.

Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three or four pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of the internal structure of trained networks could yield important contributions to the field of music cognition.

How does the brain represent musical properties? Even with our growing understanding of the cognitive neuroscience of music (Abbott, 2002; Peretz and Zatorre, 2003; Peretz and Zatorre, 2005; Zatorre and McGill, 2005), the answer to this question remains unclear. One method for conceiving possible representations is to use artificial neural networks, which can provide biologically plausible models of cognition (Rumelhart and McClelland, 1986; Bechtel and Abrahamsen, 2002; Enquist and Ghirlanda, 2005). One could train networks to solve musical problems, (Todd and Loy, 1991; Griffith and Todd, 1999) and then study how these networks encode musical properties. However, researchers rarely conduct detailed examinations of network structure (Dawson, 2009, 2013, 2018) because networks are difficult to interpret, and because it is assumed that networks capture informal or subsymbolic properties (Smolensky, 1988; McCloskey, 1991; Bharucha, 1999). Within this thesis, we report very high correlations between network connection weights and discrete Fourier phase spaces used to represent musical sets (Amiot, 2016; Callender, 2007; Quinn, 2006, 2007; Yust, 2016). This is remarkable because there is no clear mathematical relationship between network learning rules and discrete Fourier analysis (Rumelhart, Hinton et al., 1986; Dawson and Schopflocher, 1992; Amiot, 2016). That networks discover Fourier phase spaces indicates that these spaces have an important role to play outside of formal music theory. Finding phase spaces in networks raises the strong possibility that Fourier components are possible codes for musical cognition.

For the past decade, Fiske (music, U. of Western Ontario) has been using neural network models to test his theory that musical thinking can be described as a hierarchy of progressively more intricate pattern-comparison activity, and that the resulting musical realizations are limited to only three cognitive category types. He describes the development of his theory, several related experimental studies, and the neural network models he uses to test the theory. Neural network methodology can seem daunting, he admits, so he has tried to keep technical descriptions to a minimum in order to highlight his main goal: to describe and test a set of principles that appear to represent the foundation of musical understanding. Annotation : 2004 Book News, Inc., Portland, OR (booknews.com).

This volume presents the most up-to-date collection of neural network models of music and creativity gathered together in one place. Chapters by leaders in the field cover new connectionist models of pitch perception, tonality, musical streaming, sequential and hierarchical melodic structure, composition, harmonization, rhythmic analysis, sound generation, and creative evolution. The collection combines journal papers on connectionist modeling, cognitive science, and music perception with new papers solicited for this volume. It also contains an extensive bibliography of related work. Contributors Shumeet Baluja, M.I. Bellgard, Michael A. Casey, Garrison W. Cottrell, Peter Desain, Robert O. Gjerdingen, Mike Greenhough, Niall Griffith, Stephen Grossberg, Henkjan Honing, Todd Jochem, Bruce F. Katz, John F. Kolen, Edward W. Large, Michael C. Mozer, Michael P.A. Page, Caroline Palmer, Jordan B. Pollack, Dean Pomerleau, Stephen W. Smoliar, Ian Taylor, Peter M. Todd, C.P. Tsang, Gregory M. Werner

Music offers a unique opportunity to better understand the organization of the human brain. Like language, music exists in all human societies. Like language, music is a complex, rule-governed activity that seems specific to humans, and associated with a specific brain architecture. Yet unlike most other high-level functions of the human brain - and unlike language - music is a skill at which only a minority of people become proficient. The study of music as a major brain function has for some time been relatively neglected. Just recently, however, we have witnessed an explosion in research activities on music perception and performance and their correlates in the human brain. This volume brings together an outstanding collection of international authorities - from the fields of music, neuroscience, psychology, and neurology - to describe the amazing advances being made in understanding the complex relationship between music and the brain. Aimed at psychologists and neuroscientists, this is a book that will lay the foundations for a cognitive neuroscience of music.

Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena. With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections: The neural basis of cognition Development and category learning Implicit learning Social cognition Evolution Semantics Covering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.