Draft:Edward Large

• Comment: You need to heavily cut the page. At the moment it describes almost everything, not what matters. For certain 17 pubs is far too many. Any major awards? Also there is too much bragging, for instance phrases such as "has garnered international recognition". See WP:PEACOCK. Focus on what matters in WP:NPROF, remembering that brevity is better. Ldm1954 (talk) 23:14, 12 November 2024 (UTC)

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Edward Large, Ph.D.

Edward W. Large is a theoretical and cognitive neuroscientist known for his work in music cognition and the neuroscience of music. He is a Professor of Psychological Sciences and Physics at the University of Connecticut . His research uses theoretical modeling in conjunction with behavioral, neurophysiological, and neuroimaging techniques to understand how people respond to complex, temporally structured sequences of sound such as music. Large’s work on the synchronization of brain rhythms by musical rhythms has garnered international recognition .

Education

Large studied mathematics at Colorado College and classical guitar performance at Southern Methodist University, earning his BS from SMU in 1982. After several years performing in rock and fusion bands, he returned to graduate school to study Cognitive Science at The Ohio State University . His dissertation, Dynamic Representation of Musical Structure, mentored by Mari Riess Jones, Caroline Palmer and Jordan Pollack, used neural networks and dynamical systems to describe and predict how the human brain responds to music . He earned his MA and PhD in Computer and Information Science in 1994. During his graduate studies he was awarded a fellowship from the American Electronics Association, to attend Cornell University’s Far East Asian Languages Concentrated (FALCON) Program, and subsequently work at Toshiba’s Research and Design Center in Kawasaki, Japan.

Professional Career

After graduate school, Large began working at the Air Force Research Labs, where he was awarded a National Research Council Fellowship. In 1995, he joined the  Center for Cognitive Science, and the Psychology Department at the University of Pennsylvania, where he received a National Research Service Award from the National Institutes of Health. At Penn, his research spanned auditory neuroscience, music cognition, and robotics.

In 1998, Large joined the Center for Complex Systems and Brain Sciences at Florida Atlantic University, where he was promoted to Full Professor in 2012. During his time there, he received a CAREER Award from the National Science Foundation and was named Fulbright Visiting Research Chair at McGill University for 2007–08.

In 2013, Large joined UConn, where he directs the Ecological Psychology Program and the Music Dynamics Laboratory. He is active in several professional organizations and served as president of the Society for Music Perception and Cognition (SMPC) from 2013 to 2016, an organization focused on the study of musical cognition.

Large is also a founder and Chief Science Officer of Oscillo Biosciences, a company that develops music-based therapies for neurological disorders such as Alzheimer’s disease by synchronizing brain rhythms with music. In 2024, he received the Music Has Power Award from the Institute for Music and Neurologic Function for his groundbreaking scientific research and contributions at Oscillo.

Research

Large is best known for his pioneering work on Dynamic Attending Theory (DAT) with his colleague and mentor Mari Riess Jones. Large and Jones were the first to propose a link between the temporal allocation of attention and the entrainment of neural oscillations. DAT offers both a conceptual framework and computational models to explain how neural rhythms synchronize with the external world, allowing attentional coordination. In 2005, Snyder and Large published the first study showing that musical rhythms could entrain gamma rhythms in the brain using scalp EEG (). This led to collaborations with Laurel Trainor, Bernhard Ross, and Takako Fujioka, which further demonstrated the entrainment of gamma and beta oscillations (). Their work has inspired numerous labs to explore this and related phenomena (). DAT is one of the leading theories in auditory cognitive neuroscience, and the broader concept of neural entrainment by external rhythms is widely studied and documented in neuroscience literature.

In addition to DAT, Large has conducted many theoretical studies focused on developing mathematical tools to describe brain function and behavior. Along with his colleagues, he has created advanced models of neural oscillation and Hebbian learning using nonlinear dynamical systems (). These models have been applied to describe signal processing, plasticity, and pattern formation in networks of oscillatory elements, ranging from individual neurons to cortical populations. His work has made significant contributions to understanding human music perception, cognition, and development by detailing how oscillatory systems respond to sound, learn through Hebbian plasticity, and generate and recognize patterns (refs).

Large is also internationally recognized for his Neural Resonance Theory (NRT), a dynamical systems approach to music perception and cognition (). NRT builds on frameworks such as ecological perception-action and radical embodiment combining them with neural modeling. It uses principles of dynamical neuroscience to explain how brain-body dynamics physically embody musical structure. NRT's central claim is that certain types of sounds interact with ongoing neural patterns, leading to the perception, action, and coordination that people experience as music. This theory has been supported by research into various aspects of auditory and motor system function, including cochlear processing, consonance and dissonance (),  musical tonality (), rhythm perception (), and the development of musical ability ().

At Oscillo Biosciences, Large’s work extends his foundational research on the neuroscience of music to develop treatments for neurological disorders through rhythmic stimulation. Oscillo's technology aims to modulate disordered neural rhythms found in conditions like Alzheimer’s and Parkinson’s disease, using music-based interventions to restore healthy brain function.

Representative Papers

Large, E. W., Roman, I., Kim, J. C., Cannon, J., Pazdera, J. K., Trainor, L. J., Rinzel, J., Bose, A. (2023). Dynamic models for musical rhythm perception and coordination. Frontiers in Computational Neuroscience, 17, 1151895. doi: 10.3389/fncom.2023.1151895.

Tichko, P., Kim, J. C. & Large, E. W. (2022). A dynamical, radically embodied, and ecological theory of rhythm development. Frontiers in Psychology, 13 1-15. doi: 10.3389/fpsyg.2022.653696.

Kim, J. C., & Large, E. W. (2021). Multifrequency Hebbian plasticity in coupled neural oscillators. Biological Cybernetics. pp. 1-15, doi: 10.1007/s00422-020-00854-6.

Roman I. R., Washburn A., Large E. W., Chafe, C., Fujioka, T. (2019). Delayed feedback embedded in perception-action coordination cycles results in anticipation behavior during synchronized rhythmic action: A dynamical systems approach. PLoS Computational Biology, 15 (10), e1007371. doi: 10.1371/journal.pcbi.1007371.

Lerud, K. L., Kim, J. C., Almonte, F. V., Carney, L. H. & Large, E. W. (2019). A canonical oscillator model of cochlear dynamics. Hearing Research, 180, 100-107. doi: 10.1016/j.heares.2019.06.001

Kim, J. C. & Large, E. W. (2019). Mode-locking dynamics in gradient frequency neural networks, Physical Review E, 99 (2), 022421. doi: 10.1103/PhysRevE.99.022421.

Tichko, P. & Large, E. W. (2019) Modeling infants’ perceptual narrowing to musical rhythms: Neural oscillation and Hebbian plasticity, Annals of the New York Academy of Sciences. doi: 10.1111/nyas.14050.

Tal, I., Large, E. W., Rabinovitch, E., Wei, Y., Schroeder, C. E., Poeppel, D., & Zion Golumbic, E. (2017). Neural Entrainment to the Beat: The “Missing Pulse” Phenomenon. Journal of Neuroscience, 37 (26), 6331– 6341. doi: 10.1523/JNEUROSCI.2500-16.2017.

Large, E. W., Kim, J. C., Flaig, N., Bharucha, J., & Krumhansl, C. L. (2016). A neurodynamic account of musical tonality. .pdfMusic Perception. 33 (3), 319-331. doi: 10.1525/mp. 2016.33.3.319

Kim, J. C., & Large, E. W. (2015). Signal processing in periodically forced gradient frequency neural networks..pdf Frontiers in Computational Neuroscience. 9:152. doi: 10.3389/fncom. 2015.00152

Large, E. W., Herrera J. A. and Velasco M. J. (2015). Neural networks for beat perception in musical rhythm..pdf Frontiers in Systems Neuroscience. 9 (159). doi: 10.3389/fnsys.2015.00159

Lerud, K. L., Almonte, F. V., Kim, J. C. & Large, E. W. (2014). Mode-locked neural oscillation predicts human auditory brainstem responses to musical intervals..pdf Hearing Research, 308, 41-49. doi: 10.1016/j.heares.2013.09.010

Fujioka, T., Trainor, L. J., Large, E. W. & Ross, B. (2012). Internalized timing of isochronous sounds is represented in neuromagnetic beta oscillations. The Journal of Neuroscience, 32, 1791-1802.

Large, E. W. (2010). Neurodynamics of music..pdf In M. Riess Jones, R. R. Fay & A. N. Popper (Eds.), Springer Handbook of Auditory Research, Vol. 36: Music Perception (pp. 201-231). New York: Springer.

Large, E. W. (2010). Dynamics of musical tonality..pdf R. Huys and V. K. Jirsa (Eds.): Nonlinear Dynamics in Human Behavior (pp. 193–211). Berlin: Springer-Verlag.

Large, E. W. (2008). Resonating to musical rhythm: Theory and experiment..pdf In Simon Grondin, (Ed.) The Psychology of Time. West Yorkshire: Emerald.

Large, E. W., and Palmer, C. (2002). Temporal responses to music performance: Perceiving structure in temporal fluctuation..pdfCognitive Science, 26, 1-37.

Large, E. W., and Jones, M. R. (1999). The dynamics of attending: How we track time varying events..pdf Psychological Review, 106 (1), 119-159.