Odelia Schwartz, PhD
Associate professor, Computer Science
University of Miami
Computational neuroscience; natural scene statistics; machine learning
odelia [at] cs.miami.edu

We have potential openings for students with background in computational neuroscience or machine learning.


Our research is at the interface of computer science and the brain sciences. We are interested in understanding how neural systems make sense of information in the environment, resulting in complex inferences, perception, and behavior. A main focus has been building models based on the hypothesis that images, movies, and sounds have predictable and quantifiable statistical regularities to which the brain is sensitive. Our approaches are interdisciplinary, employing tools from machine learning, including unsupervised learning, and deep learning; signal processing; and mathematics. We also collaborate with experimental groups in neurophysiology, psychology, and cognitive science. We are currently pushing the notion of learning statistical regularities in visual scenes to make testable predictions about how cortical circuits process natural scenes. We are further focusing on understanding how the brain processes visual information hierarchically and the role of canonical nonlinearities. We expect our approaches will also lead to better artificial systems, that have perceptual and cognitive abilities more like humans, and can generalize better across stimuli and tasks.

We have received funding from the NSF Robust Intelligence, the NIH National Eye Institute, an NIH CRCNS (Collaborative Research in Computational Neuroscience) grant, a Google faculty research award, the Army Research Office, and an Alfred P. Sloan Research Fellowship. We have received a GPU hardware donation from NVIDIA.


Luis Gonzalo Sánchez Giraldo, postdoc
Nasir Laskar, PhD student
Joshua Bowren, PhD student

Former lab members:

Michoel Snow, MD/PhD student
Ruben Coen Cagli, postdoc (now Assistant Professor at Einstein)
Florian Röhrbein, postdoc (now in TUM’s Informatics)
Toviah Moldwin, research position (now doing PhD at Hebrew University)


  • LG Sanchez Giraldo, Md Nasir Uddin Laskar, O Schwartz. Normalization and Pooling in Hierarchical Models of Natural Images. Current Opinion in Neurobiology. Accepted, 2019.
  • LG Sanchez Giraldo, O Schwartz. Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks. arXiv preprint, 2018.
  • Md Nasir Uddin Laskar, L G Sanchez Giraldo, O Schwartz. Correspondence of Deep Neural Networks and the Brain for Visual Textures. arXiv preprint, 2018.
  • M H Turner, L G Sanchez-Giraldo, O Schwartz *, F Rieke * Stimulus- and goal-oriented frameworks for understanding natural vision. Nature Neuroscience 2018.
  • Z Xie, O Schwartz, A Prasad. Decoding of finger trajectory from ECoG using Deep Learning.J Neural Eng 2018.
  • T Moldwin, O Schwartz, E Sussman. Statistical learning of melodic patterns influences the brain’s response to wrong notes. Journal of Cognitive Neuroscience, 2017.
  • M Snow, R C Cagli, O Schwartz. Adaptation in Visual Cortex: a case for probing neural populations with natural stimuli. F1000, 2017.
  • O Schwartz, L G Sanchez Giraldo. Behavioral and neural constraints on hierarchical representations. Journal of Vision, 2017.
  • Md Nasir Uddin Laskar, L G Sanchez Giraldo, O Schwartz. Deep learning captures V2 selectivity for natural textures. Computational and Systems Neuroscience (Cosyne) abstract, 2017.
  • L G Sanchez Giraldo, O Schwartz. Flexible normalization in deep convolutional neural networks. Computational and Systems Neuroscience (Cosyne) abstract, 2017.
  • M Snow, R C Cagli, O Schwartz. Specificity and timescales of cortical adaptation as inferences about natural movie statistics. Journal of Vision (2016).
  • R M Symonds, W Lee, A Kohn, O Schwartz, S Witkowski, E S Sussman. Distinguishing Neural Adaptation and Predictive Coding Hypotheses in Auditory Change Detection. Brain Topography, 2016. doi:10.1007/s10548-016-0529-8.
  • T H Chou, W J Feuer, O Schwartz, M J Rojas, J K Roebber, V Porciatti. Integrative properties of retinal ganglion cell electrical responsiveness depend on neurotrophic support and genotype in the mouse. Experimental Eye Research 145:68-74, 2016.
  • L G Sánchez Giraldo, O Schwartz. Flexible Normalization in Deep Convolutional Neural Networks. 15th Neural Computation and Psychology Workshop on Contemporary Neural Network Models: Machine Learning, Artificial Intelligence, and Cognition. (abstract, 2016).
  • R C Cagli, A Kohn*, O Schwartz*, Flexible Gating of Contextual Modulation During Natural Vision. Nature Neuroscience, 2015.
  • The Impact on Mid-level Vision of Statistically Optimal Divisive Normalization. R C Cagli, O Schwartz. Journal of Vision, 13(8):13, 2013.
  • Attention and Flexible Normalization Pools. O Schwartz, R C Cagli. Journal of Vision, 13(1):25, 2013.
  • Attending to Visual Motion: A Realistic Dynamical Bottom-up Saliency-Based Approach. J F Ramirez-Villegas, O Schwartz, D F Ramirez-Moreno. Biological Cybernetics 2012.
  • Cortical Surround Interactions and Perceptual Salience Via Natural Scene Statistics. R C Cagli, P Dayan, O Schwartz. PLoS Computational Biology 2012, 8(3).

  • Statistical Models of Linear and Nonlinear Interactions in Early Visual Processing. R Coen-Cagli, P Dayan, and O Schwartz. Advances in Neural Information Processing Systems 22, 2009.
    Preprint pdf

  • Perceptual Organization in the Tilt Illusion. O Schwartz, T J Sejnowski, and P Dayan. Journal of Vision, 2009.

  • Visuomotor Characterization of Eye Movements in a Drawing Task. R Coen-Cagli , P Coraggio, P Napoletano, O Schwartz, M Ferraro, G Boccignone. Vision Research 49, 810-818, 2009.
    Reprint (pdf)

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

  • Visual adaptation: neural, psychological and computational aspects. Clifford, C.W.G., Webster M.A., Stanley, G.B., Stocker, A.A., Kohn, A., Sharpee, T.O. & Schwartz, O. Vision Research, 47, 3125-3131 2007.

  • Spike-triggered Neural Characterization. O Schwartz, J W Pillow, N C Rust, and E P Simoncelli. Journal of Vision, 2006.
    Reprint (pdf)
  • Soft Mixer Assignment in a Hierarchical Model of Natural Scene Statistics. O Schwartz, T J Sejnowski, and P Dayan. Neural Computation, 2006.
    Reprint (pdf)

  • A Bayesian Framework for Tilt Perception and Confidence. O Schwartz, T J Sejnowski, and P Dayan. Advances in Neural Information Processing Systems 18, 2006.
    Reprint (pdf)

  • Spatiotemporal Elements of Macaque V1 Receptive Fields. N C Rust, O Schwartz, J A Movshon, and E P Simoncelli. Neuron, 46(6):945-956, June 2005.
    Reprint (pdf)
  • Assignment of Multiplicative Mixtures in Natural Images. O Schwartz, T J Sejnowski, and P Dayan. Advances in Neural Information Processing Systems 17, 2005.
    Preprint (242K, pdf)
  • Spike count distributions, factorizability, and contextual effects in area V1. O Schwartz, J R Movellan, T Wachtler, T D.Albright, and T J Sejnowski. Neurocomputing, Elsevier, 2004.
    Preprint (168K, pdf)
  • Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1. N C Rust, O Schwartz, J A Movshon and E P Simoncelli. Neurocomputing, Elsevier, 2004.
    Preprint (750K, pdf)
  • Characterization of neural responses with stochastic stimuli. E P Simoncelli and J Pillow and L Paninski and O Schwartz. In The Cognitive Neurosciences, Ed: M Gazzaniga, 3rd edition. MIT Press, 2004.
    Preprint (1.2M, pdf)
  • Characterizing neural gain control using spike triggered covariance. O Schwartz, E J Chichilnisky, and E P Simoncelli. Adv. Neural Information Processing Systems, v14, pp. 269-276, 2002.
    Preprint (544k, pdf
  • Natural image statistics and divisive normalization: Modeling nonlinearity and adaptation in cortical neurons. M J Wainwright, O Schwartz, and E P Simoncelli. In Probabilistic Models of the Brain: Perception and Neural Function. eds. R Rao, B Olshausen, and M Lewicki. MIT Press, 2002.
    Full Text (498k, ps.gz) / Full Text (138k, pdf)
  • Modeling Surround Suppression in V1 Neurons with a Statistically-Derived Normalization Model. E P Simoncelli and O Schwartz. Adv. Neural Information Processing Systems. v11, May 1999.
    Full Text (377k, ps.gz) / Full Text (102k, pdf)
  • Modeling the Precedence Effect For Speech Using the Gamma Filter. O. Schwartz, J.G. Harris, and J.C. Principe. Neural Networks, 12(3):409-417, 1999.
  • Modeling the precedence effect for speech signals. O. Schwartz, J.G. Harris, and J.C. Principe. In Computational Neuroscience Trends in Research, Volume 4, Pages 819-826, 1998.
    Full Text (133k, ps.gz)
+ web design: Ruben Coen Cagli _ last update by Odelia: 12.2014 +