Laurent Itti

Associate Professor of Computer Science,
Adjunct Associate Professor of Psychology and Neuroscience Graduate Program

Laurent  Itti

Research Topics

  • Computational modeling
  • Biological vision
  • Computer vision
  • Functional neuroimaging
  • Human psychophysics
  • Neural networks
  • Artificial intelligence
  • Neuromorphic engineering
  • Neuromimetic algorithms

Research Images

What is the essense of surprise? Our new formal Bayesian definition of surprise provides, for the first time, a quantitative answer. Work with Prof. Pierre Baldi at the University of California, Irvine.What attracts our eyes towards some visual stimuli more than towards others? General architecture of our computational model of saliency-based visual attention. C++ source code available at http://iLab.usc.eduUsing a neurobiological model of saliency-based attention to drive the gaze of a virtual agent.General computational architecture for complex visual scene understanding in the primate brain.

Research Overview

The main fundamental research focus of the lab is in using computational modeling to gain insight into biological brain function. Thus, we study biologically-plausible brain models, and we compare the predictions of model simulations to empirical measurements from living systems. The brain subsystem towards which most of our efforts are focused is the visual system. Our modeling efforts range from fairly detailed models of small neuronal circuits, such as a single hypercolumn of orientation-selective neurons in primary visual cortex, to large-scale models embodying several million highly-simplified neurons to explore mechanisms of visual attention, gaze control, object recognition, and goal-oriented scene understanding. Further, we strive to employ modeling principles which are mathematically optimal in some task- and goal-dependent sense. Thus, we are interested in investigating the tasks and conditions for which the biological brain approaches the theoretical limits of information processing.

Our fundamental research activity includes experimental work with human subjects. One experimental technique used in the lab is visual psychophysics, with which we probe the mechanisms underlying basic visual perception by asking observers to quickly report on some attributes of simple visual patterns flashed on a computer screen. This is complemented by eye-tracking research, where we highly accurately monitor the gaze of human participants to provide an implicit behavioral response, in complement to possible explicit responses such as pressing a response button. A second experimental focus is to employ in vivo functional neuroimaging techniques to correlate brain activity to psychophysical performance, for example using functional magnetic resonance imaging (fMRI) to measure local changes in brain blood oxygenation correlated with mental activity. This neuroimaging focus is interested not only in the basic science of normal brain function, but also in the medical investigation of how such function may be altered in disease conditions. Finally, a third upcoming experimental focus is to employ electrophysiological recording to probe the activity of single neurons or small groups of neurons in the living monkey brain as well as in slice preparations from rodent brains.

Directly complementing our modeling and experimental focus on the basic science of brain function, our lab also explores a number of engineering applications of this basic research work, mainly in the fields of machine vision, image processing, robotics, and artificial intelligence. The underlying vision is our belief that, to be proven truly useful and insightful, computational neuroscience models should not only be tested against neural or behavioral data in the context of specialized laboratory experiments, but should also be exercised in the context of more general applications which confront the models to the real world. For example, we investigate whether our biologically-inspired visual models can be extended to solve problems such as automatic target detection in cluttered natural scenes, video compression, autonomous robotic nagivation on land or under water, or animation of virtual agents. We also investigate how learning and knowledge representation techniques derived from research in artificial intelligence could be used to make our models more performant at solving given machine vision tasks.

Contact Information

Mailing Address Hedco Neuroscience Building, room 30A
3641 Watt Way, MC 2520
Los Angeles, CA 90089-2520
Office Location HNB-30A
Office Phone (213) 740-3527
Lab Location HNB-06
Lab Phone
Fax (213) 740-5687
Office Location HNB-30A



  • BS, Mathematiques Superieures and Speciales M' (fundamental mathematics & physics), Lycee Descartes, Tours, France, 1991
  • MS, Ecole Nationale Superieure des Telecommunications (major: Image Processing), Paris, France, 1994
  • PhD, California Institute of Technology (major: Computation & Neural Systems), Pasadena, California, 2000

Selected Publications

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  • B. J. White, S. E. Boehnke, R. A. Marino, L. Itti, D. P. Munoz, Color-related Signals in the Primate Superior Colliculus, Journal of Neuroscience, Vol. 29, No. 39, pp. 12159-12166, Sep 2009.
  • P. F. Baldi, L. Itti, Of bits and wows: A Bayesian theory of surprise with applications to attention, Neural Networks, Vol. 23, No. 5, pp. 649-666, Jun 2010. PubMed
  • D. J. Berg, S. E. Boehnke, R. A. Marino, D. P. Munoz, L. Itti, Free viewing of dynamic stimuli by humans and monkeys, Journal of Vision, Vol. 9, No. 5:19, pp. 1-15, May 2009. PubMed
  • Navalpakkam V, Itti L. (2007) Search goal tunes visual features optimally. Neuron. 53(4):605-17. PubMed
  • Siagian C, Itti L. (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(2):300-312. PubMed
  • V. Navalpakkam, L. Itti (2006) An Integrated Model of Top-down and Bottom-up Attention for Optimal Object Detection, In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1-7, Jun. Link
  • R. J. Peters, A. Iyer, L. Itti, C. Koch (2005) Components of bottom-up gaze allocation in natural images, Vision Research, Vol. 45, No. 8, pp. 2397-2416. Link
  • V. Navalpakkam, L. Itti (2005) Modeling the influence of task on attention. Vision Research. Vol. 45, No. 2, pp. 205-231. PubMed Link
  • L. Itti, C. Koch, Computational Modelling of Visual Attention, Nature Reviews Neuroscience, Vol. 2, No. 3, pp. 194-203, Mar 2001.
  • L. Itti, C. Koch, E. Niebur, A Model of Saliency-Based Visual Attention for Rapid Scene Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, pp. 1254-1259, Nov 1998.