David A. Peterson

 

Institute for Neural Computation

University of California, San Diego

La Jolla, CA 92093-0523

dp [at] ucsd.edu

 

dave_peterson

Affiliations

I am a postdoctoral scholar in Howard Poizner's laboratory doing collaborative research with Scott Makeig at the Swartz Center for Computational Neuroscience and Terry Sejnowski at the Salk Institute for Biological Studies.

 

Research Interests

The overarching goal of my research is to advance translational developments in movement disorders. I am particularly interested in the learning- and plasticity-related dynamics of the nervous system in Parkinson's disease and dystonia. By combining theoretical, computational, and experimental approaches, I hope to identify ways to harness the brain's natural abilities to modify itself to develop better therapies for these debilitating disorders.

 

Select Publications

 

  1. Peterson DA, Elliott C, Song DD, Makeig S, Sejnowski TJ, Poizner H (in press) Probabilistic reversal learning is impaired in Parkinson's disease. Neuroscience.

 

  1. Moore KS, Peterson DA, O'Shea G, McIntosh GC, Thaut MH (2008) The effectiveness of music as a mnemonic device on recognition memory for people with multiple sclerosis. Journal of Music Therapy 45:307-329.

 

  1. Thaut MH, Peterson DA, Sena KM, McIntosh GC (2008) Musical structure facilitates verbal learning in multiple sclerosis. Music Perception 25:325-330.

 

  1. Peterson DA and Thaut MH. (2007) Music increases frontal EEG coherence during verbal learning, Neuroscience Letters 412(3): 217-221.

 

  1. Thaut MH, Peterson DA and McIntosh GC. (2005) Temporal entrainment of cognitive functions: musical mnemonics induce brain plasticity and oscillatory synchrony in neural networks underlying memory, Annals of the New York Academy of Sciences, 1060: 243-54.

 

  1. Peterson DA, Knight JN, Kirby MJ, Anderson CW, Thaut MH. (2005) Feature selection and blind source separation in an EEG-based brain-computer interface.EURASIP Journal on Applied Signal Processing; Special Issue on Trends in Brain Computer Interfaces 19: 3128-3140.

 

7.      Garrett D, Peterson DA, Anderson CW, and Thaut MH. (2003) Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering 11(2):141-144.

 

8.      Peterson DA and Thaut MH. (2002) Delay modulates spectral correlates in the human EEG of non-verbal auditory working memory. Neuroscience Letters 328: 17-20.

 

9.      Varkevisser BA, Peterson DA, Ogura T, and Kinnamon SC. (2001) Neural networks distinguish between taste qualities based on receptor cell population responses. Chemical Senses 26: 499-505.

 

My Curriculum Vitae (.pdf)