Statistical analysis of neural data.
For many years, Professor Kass worked on Bayesian statistical theory and methodology, which is now among the fastest-growing areas in the field of Statistics. Currently his group focuses on the statistical analysis of neural data, with emphasis on neural coding.
Kass and colleagues have developed methods for analyzing single spike trains involving spike counts, trial-averaged firing rates, non-Poisson spiking, and bursting; multiple spike trains, including spike count correlation, trial-to-trial variation, network effects, and synchrony detection; and decoding for brain-machine interface.
Chase, S.M., Schwartz, A.B. and Kass, R.E. Latent inputs improve estimates of neural encoding in motor cortex, Journal of Neuroscience, 30: 13873-13882, 2010.
Kelly, R.C., Smith, M.A., Kass, R.E. and Lee, T.-S. Local field potentials indicate network state and account for neuronal response variability, Journal of Computational Neuroscience, 29: 567—579, 2010.
Tokdar, S., Xi, P., Kelly, R.C., and Kass, R.E. Detection of bursts in extracellular spike trains using hidden semi-Markov point process models, Journal of Computational Neuroscience, 29: 203-212, 2009.
Behseta, S., Berdyyeva, T., Olson, C.R., and Kass, R.E. Bayesian correction for attenuation of correlation in multi-trial spike count data, Journal of Neurophysiology, 101: 2186-2193, 2009.
Kass, R.E., Ventura, V. and Brown, E.N. Statistical issues in the analysis of neuronal data, Journal of Neurophysiology, 94: 8-25, 2005.
Brown, E.N., Kass, R.E. and Mitra, P.P. Multiple neural spike trains analysis: state-of-the-art and future challenges, Nature Neuroscience, 7: 456-461, 2004.