My research sits at the interface between computational neuroscience and statistical machine learning. We develop statistical tools for making sense of high-dimensional neural datasets and seek to understand how neurons process and transmit information using spike trains. We collaborate closely with experimentalists to study neural encoding and decoding, and the transformation of neural representations between brain areas. We also develop models for the brain's ability to learn, perceive, and make decisions, and seek to uncover the theoretical principles governing the design of sensory systems.
Latimer KL, Yates JL, Meister MLR, Huk AC, & Pillow JW (2015) Single-trial spike trains in parietal cortex reveal discrete steps during decision-making Science 349(6244): 184-187.
Park IM, Meister, MLR, Huk AC, & Pillow JW (2014). Deciphering the code for sensorimotor decision-making in parietal cortex, Nature Neuroscience 17, 1395-1403.
Archer E, Park I, & Pillow JW (2014). Bayesian Entropy Estimation for Countable Discrete Distributions. Journal of Machine Learning Research 15 (Oct): 2833-2868.
Park M, Weller JP, Horwitz GD, & Pillow JW (2014). Bayesian active learning of neural firing rate maps with transformed Gaussian process priors. Neural Computation 26(8):1519-1541.
Latimer KW, Chichilnisky EJ, Rieke F, Pillow, JW (2014). Inferring synaptic conductances from spike trains with a biophysically inspired point process model. Adv. in Neur. Inf. Processing Systems 27, 954-962.