The qualifying exam will be held in June, 2008. Material from the following classes will be covered on the exam.

• 6.867 Machine learning and neural networks (2002). Lectures 2, 3, 5, 11-19, 21 (second half only), 22, 23.
• 9.011 Brain and Cognitive Sciences I. All lectures.
• 9.29 Introduction to computational neuroscience (2002). Lectures 1-11, 14-16, 23-24.
• 9.343 Cognitive artifacts and architectures (2005). Only the bold-faced readings in the syllabus are required.
• 9.35 Sensation and perception (2006). All lectures and additional readings below.
  
• Sekuler and Blake, Perception (4th edition) Chapters 1-9.
• Landy and Movshon, Computational Models of Visual Processing, MIT Press 1991:
• Chapter 1: Adelson: The Plenoptic Function and the Elements of Early Vision
• Chapter 8:Shapley et al: Spatiotemporal Receptive Fields and Direction Selectivity
• Chapter 9: Heeger: Nonlinear Model of Neuronal Responses in Cat Visual Cortex
• Chapter 17: Bergen & Landy: Computational Modeling of Visual Texture Segregation
• Chapter 20: Buelthoff: Shape from X.
• B.A. Wandell: Foundations of Vision, Sinauer Associates (1995)
• Chapter 8: Multiresolution Image Representations
• Chapter 9: Color
• Chapter 10: Motion and Depth
• 9.520 Statistical learning theory and applications (Spring 2004). Lectures 2-5
  • T. Evgeniou and M. Pontil and T. Poggio. Regularization Networks and Support Vector Machines. Advances in Computational Mathematics, 2000.
  • T. Poggio and S. Smale. The Mathematics of Learning: Dealing with Data. Notices of the AMS, 2003
  • Poggio, T., R. Rifkin, S. Mukherjee and P. Niyogi. General Conditions for Predictivity in Learning Theory, Nature, Vol. 428, 419-422, 2004.
  • Serre, T., M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman and T. Poggio. A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , CBCL Paper #259/AI Memo #2005-036, Massachusetts Institute of Technology, Cambridge, MA, October, 2005.
• 9.641 Introduction to neural networks (2002). Lectures 2-4, 8-10, 12-13, 16-19.
• 9.670 Object and face recognition.
  • Lecture 3: Primate object recognition
  • Lecture 4: Specialization for face recognition
  • Lecture 5: Processes underlying face recognition
  • Lecture 8: Notable models of object and face recognition
  • Lecture 10: Classical pattern recognition theory
  • Biederman, I. (1987). Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review, 94, 115-147.
  • Poggio, T., and S. Edelman, A network that learns to recognize three-dimensional objects, Nature, 343:263-266, Jan. 1990.
  • Gauthier, I. & Logothetis, N. (2000). Is face recognition not so unique after all? Cognitive Neuropsychology, 17(1/2/3), 125-142.
  • Brunelli, R. and Poggio, T. (1993). Face Recognition: Features vs. Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10):1042-1052.
  • Daugman, J. (1997). Face and Gesture Recognition: Overview. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):675-676.
  • Hancock, P.J.B., Burton, A.M., and Bruce, V. (1996). Face processing: Human perception and principal components analysis. Memory and Cognition, 24(1):26-40.
  • Turk, M. and Pentland, A.P. (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1):71-86.
  • Fukushima, K., Miyake, S., and Ito, T. (1983), "Neocognitron: A neural network model for a mechanism of visual pattern recognition", IEEE Transactions on Systems, Man, and Cybernetics, 13.
  • Richard Duda and Peter Hart. Pattern Recognition and Scene Analysis. John Wiley and Sons, 1973. (sections on unsupervised learning/clustering and nonparametric techniques for classification)

• The Theory of Point Processes for Neural Systems
  • Brown EN, Barbieri R, Eden UT, Frank LM. Likelihood methods for neural spike train data analysis. In: Feng J, ed. Computational Neuroscience: A Comprehensive Approach, London: CRC, 2003, pp. 253-86.
  • Brown EN. Theory of Point Processes for Neural Systems. In: Chow CC, Gutkin B, Hansel D, Meunier C, Dalibard J, eds. Methods and Models in Neurophysics. Paris, Elsevier; 2005, Chapter 14, pp. 691-726.
• State-Space Modeling of Neural Systems
  • Smith AC, Brown EN. Estimating a state-space model from point process observations. Neural Computation, 2003, 15: 965-91.
  • Eden UT, Frank LM, Barbieri R, Solo V, Brown EN, Dynamic analyses of neural encoding by point process adaptive filtering. Neural Computation, 2004, 16(5): 971-998.
  • Smith AC, Frank LM, Wirth S, Yanike M, Hu D, Kubota Y, Graybiel AM, Suzuki WA, Brown EN. Dynamic analysis of learning in behavioral experiments. Journal of Neuroscience, 2004, 24(2): 447-461.