Microsoft Word Kurzweil, Ray The Singularity Is Near doc

Scientific American
256 (1987): 46–52. Also C. Koch and T. 
Poggio, "Biophysics of Computational Systems: Neurons, Synapses, and Membranes," in 
Synaptic Function
, 
G. M. Edelman, W. E. Gall, and W. M. Cowan, eds. (New York: John Wiley and Sons, 1987), pp. 637–97. 
13.
On Mead, see http://www.technology.gov/Medal/2002/bios/Carver_A._Mead.pdf. Carver Mead, 
Analog VLSI 
and Neural Systems
(Reading, Mass.: Addison-Wesley, 1986). 
14.
See note 172 in chapter 5 for an algorithmic description of a self-organizing neural net and note 175 in chapter 
5 for a description of a self-organizing genetic algorithm. 
15.
See Gary Dudley et al., "Autonomic Self-Healing Systems in a Cross-Product IT Environment," 
Proceedings 
of the IEEE International Conference on Autonomic Computing
, New York City, May 17–19, 2004, 

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http://csdl.computer.org/comp/proceedings/icac/2004/2114/00121140312.pdf; "About IBM Autonomic 
Computing," http://www-3.ibm.com/autonomic/about.shtml; and Ric Telford, "The Autonomic Computing 
Architecture," April 14, 2004, http://www.dcs.st-andrews.ac.uk/undergrad/current/dates/disclec/2003–
2/RicTelfordDistinguished2.pdf. 
16.
Christine A. Skarda and Walter J. Freeman, "Chaos and the New Science of the Brain," 
Concepts in 
Neuroscience
1.2 (1990): 275–85. 
17.
C. Geoffrey Woods, "Crossing the Midline," 
Science
304.5676 (June 4, 2004): 1455–56; Stephen Matthews, 
"Early Programming of the Hypothalamo-Pituitary-Adrenal Axis," 
Trends in Endocrinology and Metabolism
13.9 (November 1,2002): 373–80; Justin Crowley and Lawrence Katz, "Early Development of Ocular 
Dominance Columns," 
Science
290.5495 (November 17. 2000): 1321–24; Anna Penn et al., "Competition in 
the Retinogeniculate Patterning Driven by Spontaneous Activity," 
Science
279.5359 (March 27, 1998): 2108–
12; M. V. Johnston et al., "Sculpting the Developing Brain," 
Advances in Pediatrics
48 (2001): 1–38; P.La 
Cerra and R. Bingham, "The Adaptive Nature of the Human Neurocognitive Architecture: An Alternative 
Model," 
Proceedings of the National Academy of Sciences
95 (September 15, 1998): 11290–94. 
18.
Neural nets are simplified models of neurons that can self-organize and solve problems. See note 172 in 
chapter 5 for an algorithmic description of neural nets. Genetic algorithms are models of evolution using 
sexual reproduction with controlled mutation rates. See note 175 in chapter 5 for a detailed description of 
genetic algorithms. Markov models are products of a mathematical technique that are similar in some respects 
to neural nets. 
19.
Aristotle, 
The Works of Aristotle
, trans. W. D. Ross (Oxford: Clarendon Press, 1908–1952 (see, in particular, 
Physics
); see also http://www.encyclopedia.com/html/section/aristotl_philosophy.asp. 
20.
E. D. Adrian, 
The Basis of Sensation: The Action of Sense Organs
(London: Christophers, 1928). 
21.
A. L. Hodgkin and A. F. Huxley, "Action Potentials Recorded from Inside a Nerve Fibre," 
Nature
144 (1939): 
710–12. 
22.
A. L. Hodgkin and A. F. Huxley, "A Quantitative Description of Membrane Current and Its Application to 
Conduction and Excitation in Nerve," 
Journal of Physiology
117 (1952): 500–544. 
23.
W. S. McCulloch and W. Pitts, "A Logical Calculus of the Ideas Immanent in Nervous Activity," 
Bulletin of 
Mathematical Biophysics
5 (1943): 115-33. This seminal paper is a difficult one to understand. For a clear 
introduction and explanation, see "A Computer Model of the Neuron," the Mind Project, Illinois State 
University, http://www.mind.ilstu.edu/curriculum/perception/mpneuron1.html. 
24.
See note 172 in chapter 5 for an algorithmic description of neural nets. 
25.
E. Salinas and P. Thier, "Gain Modulation: A Major Computational Principle of the Central Nervous System,"