Dr. Odelia Schwartz

Assistant Professor
Sensory coding; computational neuroscience

Kennedy Center

 

 

 

We continually interact with stimuli, such as images and sounds, and make inferences about a complex world. How our brain represents and processes the information internally is an intriguing and fundamental issue at the interface of neuroscience and computation. Our lab employs tools of computational and theoretical neuroscience, to study systems from the neural level and through to perception and behavior.

We develop computational models of sensory neural processing based on the hypothesis that images and sounds have predictable and quantifiable regularities to which the brain is sensitive. The models are constructed through interplay with physiological and psychophysical data, and posit functional roles about neural processing. Additionally, a critical way to make progress is utilizing computational tools directly in experimental design and analysis. For example, we have worked extensively on spike-triggered approaches, leading to richer, non-linear characterization of neurons in retina and cortex.

Current specific interests include: (1) how neurons and percepts are affected by contextual information: spatially, what surrounds a given feature or object; temporally, what we have observed in the past, i.e., adaptation; (2) how neurons represent information hierarchically from one level of neural processing to the next; (3) how populations of neurons work together to achieve perception and behavior; and (4) how we decide where to look next in images.

Selected Publications

Schwartz, O., Hsu, A., Dayan, P. (2007) Space and time in visual context. Nature Reviews Neuroscience 8: 522-535.

Schwartz, O., Sejnowski, T.J., Dayan, P. (2006) Soft mixer assignment in a hierarchical generative model of natural scene statistics. Neural Computation 18:2680-2718.

Schwartz, O., Pillow, J.W., Rust, N.C., Simoncelli, E.P. (2006) Spike-triggered neural characterization. Journal of Vision 6(4): 484–507.

Schwartz, O., Sejnowski, T.J., Dayan, P. (2006) A Bayesian framework for tilt perception and confidence. Advances in Neural Information Processing Systems 18: 1201–1208.

Rust, N.C., Schwartz, O., Movshon, J.A., Simoncelli, E.P. (2005) Spatiotemporal elements of macaque V1 receptive fields. Neuron 46(6): 945–956.

Schwartz, O., Movellan, J.R., Wachtler, T., Albright, T.D., Sejnowski, T.J. (2004) Spike count distributions, factorizability, and contextual effects in area V1. Neurocomputing 58-60C: 893-900.

Simoncelli, E.P, Pillow, J.W., Paninski, L., Schwartz, O. (2004) Characetrization of neural responses with stochastic stimuli. In The Cognitive Sciences, Ed: M Gazzaniga, 3rd edition, MIT Press.

Schwartz, O., Chichilnisky, E.J., Simoncelli, E.P. (2002) Characterizing gain control in neurons using spike-triggered covariance. Advances in Neural Information Processing Systems 14: 269–276.

Schwartz, O., Simoncelli, E.P. (2001) Natural signal statistics and sensory gain control. Nature Neuroscience 4(8): 819–825.