VS265: Reading: Difference between revisions
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* '''HKP''' chapter 5, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6) | * '''HKP''' chapter 5, '''DJCM''' chapters 38-40, 44, '''DA''' chapter 8 (sec. 4-6) | ||
* [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models] | * [http://redwood.berkeley.edu/vs265/linear-neuron/linear-neuron-models.html Linear neuron models] | ||
Background on linear algebra: | Background on linear algebra: | ||
* [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | * [http://redwood.berkeley.edu/vs265/linear-algebra/linear-algebra.html Linear algebra primer] | ||
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* Rhodes P (1999) [http://redwood.berkeley.edu/vs265/Rhodes-review.pdf Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons] | * Rhodes P (1999) [http://redwood.berkeley.edu/vs265/Rhodes-review.pdf Functional Implications of Active Currents in the Dendrites of Pyramidal Neurons] | ||
* Schiller J (2003) [http://redwood.berkeley.edu/vs265/Schiller-spikes-dendrites.pdf Submillisecond Precision of the Input–Output Transformation Function Mediated by Fast Sodium Dendritic Spikes in Basal Dendrites of CA1 Pyramidal Neurons] | * Schiller J (2003) [http://redwood.berkeley.edu/vs265/Schiller-spikes-dendrites.pdf Submillisecond Precision of the Input–Output Transformation Function Mediated by Fast Sodium Dendritic Spikes in Basal Dendrites of CA1 Pyramidal Neurons] | ||
==== Sept. 16 ==== | |||
* [http://redwood.berkeley.edu/vs265/superlearn_handout1.pdf Handout] on supervised learning in single-stage feedforward networks | |||
* [http://redwood.berkeley.edu/vs265/superlearn_handout2.pdf Handout] on supervised learning in multi-layer feedforward networks - "back propagation" | |||
Further reading: | |||
* Y. LeCun, L. Bottou, G. Orr, and K. Muller (1998) [http://redwood.berkeley.edu/vs265/lecun-98b.pdf "Efficient BackProp,"] in Neural Networks: Tricks of the trade, (G. Orr and Muller K., eds.). | |||
* [http://cnl.salk.edu/Research/ParallelNetsPronounce/ NetTalk demo] | |||
Revision as of 03:42, 16 September 2014
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:
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
- 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
- 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
