9.641 Neural Networks

Lecture schedule

• Lecture 1 (2/1): From spikes to rates (Postscript, PDF, realvideo)
  • Christof Koch, Biophysics of Computation, Section 14.2: 335-341, Oxford University Press, New York, Oxford (1999).
  • Bard Ermentrout, Reduction of Conductance-Based Models with Slow Synapses to Neural Nets, Neural Computation. 6,679-695 (1995).
• Lecture 2 (2/3): Perceptrons: simple and multilayer (realvideo)
• Lecture 3 (2/8): Perceptrons as models of vision (realvideo)
  
  • D. Marr, Vision, Section 2.2, 54-79, W.H. Freeman and Company, New York (1982).
  • D.H. Hubel, Eye, Brain, and Vision, Chapter 3, 39-46, Scientific American Library, New York, (1988-1995).
  • Lenet website
• Lecture 4 (2/10): Linear networks (realvideo)
• Lecture 5 (2/15): Retina (realvideo)
  • E.H. Adelson, Lightness Perception and Lightness Illusions, In The New Cognitive Neurosciences, 2nd ed., M. Gazzaniga, ed. Cambridge, MA: MIT Press, pp. 339-351, (2000).
  • H. K. Hartline and F. Ratliff, Inhibitory Interaction in the Retina of Limulus. Physiology of Photoreceptor Organs, Ed. M.G.F. Fuortes, 382-447, Springer-Verlag, Berlin, Heidelberg, New York (1972)
• Lecture 6 (2/17): Lateral inhibition and feature selectivity (slides 1, slides 2, slides 3, realvideo)
  • W. H. Press et al., Numerical Recipes in C, 2d ed., Chaps. 12 and 13.
  • G. Strang, Introduction to Applied Mathematics, Section 4.2, 290-309, Wellesley-Cambridge Press, Wellesley, Massachusetts (1986).
• Lecture 7 (2/24): Objectives and optimization (realvideo)
• Lecture 8 (3/1): Hybrid analog-digital computation, ring network (realvideo)
  • R. Hahnloser, R. Sarpeshkar, M. Mahowald, R.J. Douglas, H.S. Seung, Digital selection and analog amplification coexist in a cortex-inspired silicon circuit, Nature, 405, 947-51, 2000.
  • Richard H. R. Hahnloser, H. Sebastian Seung, Jean-Jacques Slotine: Permitted and Forbidden Sets in Symmetric Threshold-Linear Networks. Neural Computation 2003.
• Lecture 9 (3/3): Constraint satisfaction, stereopsis (realvideo)
• Lecture 10 (3/8): Bidirectional perception (realvideo)
• Lecture 11 (3/10): Signal reconstruction (realvideo)
• Lecture 12 (3/15): Hamiltonian dynamics (slides, realvideo)


• Midterm (3/17)


• Lecture 13 (3/29): Antisymmetric networks (slides, realvideo)
• Lecture 14 (3/31): Excitatory-Inhibitory networks, learning (slides, realvideo)
• Lecture 15 (4/5): Associative memory (realvideo)
• Lecture 16 (4/7): Models of delay activity, Integrators (realvideo)
• Lecture 17 (4/12): Multistability, Clustering (realvideo)
• Lecture 18 (4/14): VQ and PCA (slides 1, slides 2, realvideo)
• Lecture 19 (4/21): More PCA, Delta rule (slides, realvideo)
• Lecture 20 (4/26): Conditioning and Backpropagation (slides 1, slides 2, realvideo)
• Lecture 21 (4/28): More Backpropagation (slides, realvideo)
• Lecture 22 (5/3): Stochastic Gradient Descent (realvideo)
• Lecture 23 (5/5): Reinforcement Learning (realvideo)
• Lecture 24 (5/10): More Reinforcement Learning (realvideo)
• Lecture 25 (5/12): Final Review (realvideo)