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