Brain & AI Lab


  • X Pan, R Coen-Cagli*, O Schwartz*. Probing the Structure and Functional Properties of the Dropout-induced Correlated Variability in Convolutional Neural Networks. Neural Computation, 2023 (accepted).
  • X Pan, A DeForge, O Schwartz. Generalizing biological surround suppression based on center surround similarity via deep neural network models. PLoS Computational Biology, 2023 (accepted).
  • J Bowren, L G Sanchez Giraldo, O Schwartz. Inference via sparse coding in a hierarchical vision model. Journal of Vision 22(2):19. doi: 10.1167/jov.22.2.19, 2022.
  • E B James*, X Pan*, O Schwartz, A C C Wilson. SymbiQuant: A Machine Learning Object Detection Tool for Polyploid Independent Estimates of Endosymbiont Population Size. Frontiers Microbiology.13:816608. doi: 10.3389/fmicb.2022.816608, 2022.
  • X Pan, E Kartal, LGonzalo Giraldo, O Schwartz, “Brain-Inspired Weighted Normalization for CNN Image Classification,” International Conference on Learning Representations (ICLR) Workshop: How Can Findings About The Brain Improve AI Systems, 2021.
  • Md Nasir Uddin Laskar, L G Sanchez Giraldo, O Schwartz. Deep Neural Networks Capture Texture Sensitivity in V2. Journal of Vision, pp. 1-23, 2020.
  • HL Radabaugh, J Bonnell, O Schwartz, D Sarkar, W D Dietrich, H Bramlett. Operation Brain Trauma Therapy (OBTT): the use of machine learning to re-assess patterns of multivariate functional recovery following fluid percussion injury. Journal of Neurotrauma, accepted 2020.
  • HL Radabaugh, J Bonnell, WD Dietrich, HM Bramlett, O Schwartz, D Sarkar, Development and Evaluation of Machine Learning Models for Recovery Prediction after Treatment for Traumatic Brain Injury, 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, pp. 2416-2420, 2020.
  • LG Sanchez Giraldo, Md Nasir Uddin Laskar, O Schwartz. Normalization and Pooling in Hierarchical Models of Natural Images. Current Opinion in Neurobiology, 2019.
  • M H Turner, L G Sanchez-Giraldo, O Schwartz *, F Rieke * Stimulus- and goal-oriented frameworks for understanding natural vision. Nature Neuroscience, 2019.
  • LG Sanchez Giraldo, O Schwartz. Integrating Flexible Normalization into Mid-Level Representations of Deep Convolutional Neural Networks. Neural Computation, accepted 2019.
  • Md Nasir Uddin Laskar, L G Sanchez Giraldo, O Schwartz. Correspondence of Deep Neural Networks and the Brain for Visual Textures. arXiv preprint, 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.
  • F Sikder, D Sarkar, O Schwartz, C Thomas. Method for Concurrent Processing of EMG Signals from Multiple Channels for Identification of Spasms, IEEE SPMB Proceedings, 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. Abstract.
  • L G Sanchez Giraldo, O Schwartz. Flexible normalization in deep convolutional neural networks. Computational and Systems Neuroscience (Cosyne) abstract, 2017. Abstract.
  • M Snow, R C Cagli, O Schwartz. Specificity and timescales of cortical adaptation as inferences about natural movie statistics. Journal of Vision (2016).
  • 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.
  • 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.
  • 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 8(11):1648-55, 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 Coen-Cagli, P Dayan, and O Schwartz. PLoS Computational Biology, 8(3) 2013. e1002405.
  • 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 (480K, 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)
  • 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, May 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. Spring, 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.
  • O. Schwartz, J G Harris, and J C Principe. Modeling the precedence effect for speech signals. In Computational Neuroscience Trends in Research, Volume 4, Pages 819-826, 1998.
    Full Text (133k, ps.gz