VS265: Reading

Aug 28: Introduction

• HKP chapter 1
• Dreyfus, H.L. and Dreyfus, S.E. Making a Mind vs. Modeling the Brain: Artificial Intelligence Back at a Branchpoint. Daedalus, Winter 1988.
• Bell, A.J. Levels and loops: the future of artificial intelligence and neuroscience. Phil Trans: Bio Sci. 354:2013--2020 (1999) here or here
• 1973 Lighthill debate on future of AI

Optional:

• Land, MF and Fernald, RD. The Evolution of Eyes, Ann Revs Neuro, 1992.
• Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. PNAS, 97: 5621–5626.
• O'Rourke, N.A et al. "Deep molecular diversity of mammalian synapses: why it matters and how to measure it." Nature Reviews Neurosci. 13, (2012)
• Stephen Smith Array Tomography movies
• Solari & Stoner, Cognitive Consilience

Sept 2: Neuron models

• Mead, C. Chapter 1: Introduction and Chapter 4: Neurons from Analog VLSI and Neural Systems, Addison-Wesley, 1989.
• Carandini M, Heeger D (1994) Summation and division by neurons in primate visual cortex. Science, 264: 1333-1336.

Background reading on dynamics, linear time-invariant systems and convolution, and differential equations:

• Dynamics
• Linear time-invariant systems and convolution
• Simulating differential equations

Sept 4: Linear neuron, Perceptron

• HKP chapter 5, DJCM chapters 38-40, 44, DA chapter 8 (sec. 4-6)
• Linear neuron models

Background on linear algebra:

• Linear algebra primer
• Jordan, M.I. An Introduction to Linear Algebra in Parallel Distributed Processing in McClelland and Rumelhart, Parallel Distributed Processing, MIT Press, 1985.

Sept 11: Multicompartment models, dendritic integration (Rhodes guest lecture)

• Koch, Single Neuron Computation, Chapter 19 pdf
• Rhodes P (1999) Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons
• Schiller J (2003) Submillisecond Precision of the Input–Output Transformation Function Mediated by Fast Sodium Dendritic Spikes in Basal Dendrites of CA1 Pyramidal Neurons

Sept. 16, 18: Supervised learning

• HKP Chapters 5, 6
• Handout on supervised learning in single-stage feedforward networks
• Handout on supervised learning in multi-layer feedforward networks - "back propagation"

Further reading:

• Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) "Efficient BackProp," in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.).
• NetTalk demo

Sept. 23, 24: Unsupervised learning

• HKP Chapters 8 and 9, DJCM chapter 36, DA chapter 8, 10
• Handout: Hebbian learning and PCA
• PDP Chapter 9 (full text of Michael Jordan's tutorial on linear algebra, including section on eigenvectors)

Optional:

• Atick, Redlich. What does the retina know about natural scenes?, Neural Computation, 1992.
• Dan, Atick, Reid. Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory, J Neuroscience, 1996.

Sept 30, Oct 2: Attractor Networks and Associative Memories (Sommer guest lectures)

• "HKP" Chapter 2 and 3 (sec. 3.3-3.5), 7 (sec. 7.2-7.3), DJCM chapter 42, DA chapter 7
• Handout on attractor networks - their learning, dynamics and how they differ from feed-forward networks
• Hopfield82
• Hopfield84
• Willshaw69

Oct 7: Ecological utility and the mythical neural code (Feldman guest lecture)

• Feldman10 Ecological utility and the mythical neural code

Oct 9: Hyperdimensional computing (Kanerva guest lecture)

• Kanerva, Computing with 10,000-bit words
• Kanerva, Hyperdimensional Computing

Oct 16: Structural and Functional Connectomics (Tom Dean guest lecture)

• Lecture notes and slides

21 Oct

• Barlow, HB. Single units and sensation: A neuron doctrine for perceptual psychology? Perception, volume 1, pp. 371 -394 (1972)
• Foldiak, P. Forming sparse representations by local anti-Hebbian learning. Biol. Cybern. 64, 165-170 (1990).
• Olshausen BA, Field DJ. Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, 381: 607-609. (1996)

Additional readings:

• Rozell, Johnson, Baraniuk, Olshausen. Sparse Coding via Thresholding and Local Competition in Neural Circuits, Neural Computation 20, 2526–2563 (2008).
• Zylberberg, Murphy, DeWeese, A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields., PLOS Computational Biology, 7, (2011). (sparse coding with spiking neurons)
• Olshausen Sparse coding of time-varying natural images, ICIP 2003. (convolution sparse coding of video)
• Smith E, Lewicki MS. Efficient auditory coding, Nature Vol 439 (2006). (convolution sparse coding of sound)

23 Oct

• HKP chapter 9, DA chapter 8
• Ocular dominance column development: Analysis and simulation by Miller, Keller and Stryker.
• A dimension reduction framework for understanding cortical maps by R. Durbin and G. Mitchison.
• The cortical column: a structure without a function by Jonathan C. Horton and Daniel L. Adams

Optional:

• Gary Blasdel, Orientation selectivity, preference, and continuity in monkey striate cortex., J Neurosci, 1992. Another source of many of nice images are in the galleries on Amiram Grinvald's site: [1]
• From Clay Reid's lab, Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Make sure you look at the supplementary material and videos on their web site (seems partly broken) [2].