9.641 Neural Networks
Lecture schedule
- Lecture 1 (9/7): Class introduction and description
(PDF) and the rate equations (PDF)
- Introduction to Neurons and Classical Neurodynamics by Prof. Seung (PDF)
- Older notes by Prof. Seung (PDF)
- 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).
- Dayan & Abbott, Chapter 5, Theoretical Neuroscience (MIT only).
- Lecture 2 (9/14): Synaptic Organization (PDF) and Perceptrons (PDF)
- Lecture 3 (9/19): Vision and Computation. (Multi-layer Perceptrons: PDF, Convolutional Neural Networks / Vision:
PDF)
- 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).
- Lecture 4 (9/21): Hierarchical Visual Processing and Convolutional Networks
- Lecture 5 (9/26): Conditioning (PDF), Hebbian Learning (PDF), and the Delta Rule (PDF)
- Lecture 6 (9/28): Backpropagation (PDF)
- Older notes on delta rule (PDF) and backpropagation (PDF) by Prof. Seung
- Yann LeCun, Leon Bottou, et al. Efficient backprop, in G. Orr and K. Muller, Neural networks: tricks of the trade, Springer, 1998.
- David A. Robinson, Implications of neural networks for how we think about brain function, Behav. Brain. Sci., 15, 644-55 (1992).
- Review of the
multidimensional chain rule
- C. M. Bishop's
derivation of backpropagation, with an example and some notes on the computational
complexity of backprop. from Neural Networks for Pattern Recognition, Oxford, 1995.
- Lecture 7 (10/3): More Backpropagation, Backpropagation in Recurrent Networks (PDF)
- F. Pineda, "Generalization of Backpropagation to Recurrent Neural Networks", Physical
Review Letters, Vol. 59, No. 19, 1987.
- Lecture 8 (10/5): Symmetric Networks (PDF)
- Lecture 9 (10/12): Contrastive Hebbian Learning
(PDF); Stochastic Networks (Boltzmann Machine)
and the Mean-Field Approximation
(PDF)
- Movellan, J. (1990). Contrastive Hebbian learning in the continuous Hopfield
model. In D. Touretzky, J. Elman, T. Sejnowski,&G. Hinton (Eds.), Proceedings
of the 1990 Connectionist Models Summer School, San Mateo, CA:
Morgan Kaufmann. (PDF)
- Movellan, J. (1990). Contrastive Hebbian Learning in Interactive Networks.
(unpublished). (PDF)
- X. Xie and H. S. Seung. Equivalence of backpropagation and contrastive Hebbian learning in a layered network. Neural Computation 15, 441-54 (2003).
(PDF)
- Lecture 11 (10/17):
- Lecture 12 (10/19): Computer Vision; Edge Detection
- Lecture 13 (10/24): Viren "Convolution" Jain on Convolutional Networks (PDF)
Last Updated on Monday, 30-Oct-2006 13:05:38 EST .