by Ray Kurzweil
83. J. L. Raymond, S. G. Lisberger, and M. D. Mauk, “The Cerebellum: A Neuronal Learning Machine?” Science 272.5265 (May 24, 1996): 1126–31; J. J. Kim and R. F. Thompson, “Cerebellar Circuits and Synaptic Mechanisms Involved in Classical Eyeblink Conditioning,” Trends in Neuroscience 20.4 (April 1997): 177–81.
84. The simulation included 10,000 granule cells, 900 Golgi cells, 500 mossy fiber cells, 20 Purkinje cells, and 6 nucleus cells.
85. J. F. Medina et al., “Timing Mechanisms in the Cerebellum: Testing Predictions of a Large-Scale Computer Simulation,” Journal of Neuroscience 20.14 (July 15, 2000): 5516–25; Dean Buonomano and Michael Mauk, “Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Reponses,” Neural Computation 6.1 (1994): 38–55.
86. Medina et al., “Timing Mechanisms in the Cerebellum.”
87. Carver Mead, Analog VLSI and Neural Systems (Boston: Addison-Wesley Longman, 1989).
88. Lloyd Watts, “Visualizing Complexity in the Brain,” in Computational Intelligence: The Experts Speak, D. Fogel and C. Robinson, eds. (Hoboken, N.J.: IEEE Press/Wiley, 2003), pp. 45–56, http://www.lloydwatts.com/wcci.pdf.
89. Ibid.
90. See http://www.lloydwatts.com/neuroscience.shtml. NanoComputer Dream Team, “The Law of Accelerating Returns, Part II,” http://nanocomputer.org/index.cfm? content=90&Menu=19.
91. See http://info.med.yale.edu/bbs/faculty/she_go.html.
92. Gordon M. Shepherd, ed., The Synaptic Organization of the Brain, 4th ed. (New York:Oxford University Press, 1998), p. vi.
93. E. Young, “Cochlear Nucleus,” in ibid., pp. 121–58.
94. Tom Yin,“Neural Mechanisms of Encoding Binaural Localization Cues in the Auditory Brainstem,” in D. Oertel, R. Fay, and A. Popper, eds., Integrative Functions in the Mammalian Auditory Pathway (New York: Springer-Verlag, 2002), pp. 99–159.
95. John Casseday, Thane Fremouw, and Ellen Covey, “The Inferior Colliculus: A Hub for the Central Auditory System,” in Oertel, Fay, and Popper, Integrative Functions in the Mammalian Auditory Pathway, pp. 238–318.
96. Diagram by Lloyd Watts, http://www.lloydwatts.com/neuroscience.shtml, adapted from E. Young,“Cochlear Nucleus” in G. Shepherd, ed., The Synaptic Organization of the Brain, 4th ed. (New York: Oxford University Press, 2003 [first published 1998]), pp. 121–58; D. Oertel in D. Oertel, R. Fay, and A. Popper, eds., Integrative Functions in the Mammalian Auditory Pathway (New York: Springer-Verlag, 2002), pp. 1–5; John Casseday, T. Fremouw, and E. Covey, “Inferior Colliculus” in ibid.; J. LeDoux, The Emotional Brain (New York: Simon & Schuster, 1997); J. Rauschecker and B. Tian,“Mechanisms and Streams for Processing of ‘What’ and ‘Where’ in Auditory Cortex,” Proceedings of the National Academy of Sciences 97.22: 11800–11806.
Brain regions modeled:
Cochlea: Sense organ of hearing. Thirty thousand fibers convert motion of the stapes into spectrotemporal representations of sound.
MC: Multipolar cells. Measure spectral energy.
GBC: Globular bushy cells. Relay spikes from the auditory nerve to the lateral superior olivary complex (includes LSO and MSO). Encoding of timing and amplitude of signals for binaural comparison of level.
SBC: Spherical bushy cells. Provide temporal sharpening of time of arrival, as a preprocessor for interaural time-difference calculation (difference in time of arrival between the two ears, used to tell where a sound is coming from).
OC:Octopus cells. Detection of transients.
DCN: Dorsal cochlear nucleus. Detection of spectral edges and calibrating for noise levels.
VNTB: Ventral nucleus of the trapezoid body. Feedback signals to modulate outer hair-cell function in the cochlea.
VNLL, PON: Ventral nucleus of the lateral lemniscus; peri-olivary nuclei: processing transients from the OC.
MSO: Medial superior olive. Computing interaural time difference.
LSO: Lateral superior olive. Also involved in computing interaural level difference.
ICC: Central nucleus of the inferior colliculus. The site of major integration of multiple representations of sound.
ICx: Exterior nucleus of the inferior colliculus. Further refinement of sound localization.
SC: Superior colliculus. Location of auditory/visual merging.
MGB: Medial geniculate body. The auditory portion of the thalamus.
LS: Limbic system. Comprising many structures associated with emotion, memory, territory, et cetera.
AC:Auditory cortex.
97. M. S. Humayun et al., “Human Neural Retinal Transplantation,” Investigative Ophthalmology and Visual Science 41.10 (September 2000): 3100–3106.
98. Information Science and Technology Colloquium Series, May 23, 2001, http://isandtcolloq.gsfc.nasa.gov/spring2001/speakers/poggio.html.
99. Kah-Kay Sung and Tomaso Poggio, “Example-Based Learning for View-Based Human Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence 20.1 (1998): 39–51, http://portal.acm.org/citation.cfm?id=275345&dl= ACM&coll=GUIDE.
100. Maximilian Riesenhuber and Tomaso Poggio, “A Note on Object Class Representation and Categorical Perception,” Center for Biological and Computational Learning, MIT, AI Memo 1679 (1999), ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1679.pdf.
101. K. Tanaka, “Inferotemporal Cortex and Object Vision,” Annual Review of Neuro-science 19 (1996): 109–39; Anuj Mohan, “Object Detection in Images by Components,” Center for Biological and Computational Learning, MIT, AI Memo 1664 (1999), http://citeseer.ist.psu.edu/cache/papers/cs/12185/ftp:zSzzSzpublications. ai.mit.eduzSzai-publicationszSz1500–1999zSzAIM-1664.pdf/mohan99object.pdf; Anuj Mohan, Constantine Papageorgiou, and Tomaso Poggio, “Example-Based Object Detection in Images by Components,” IEEE Transactions on Pattern Analysis and Machine Intelligence 23.4 (April 2001), http://cbcl.mit.edu/projects/cbcl/publications/ps/mohan-ieee.pdf; B. Heisele, T. Poggio, and M. Pontil, “Face Detection in Still Gray Images,” Artificial Intelligence Laboratory, MIT, Technical Report AI Memo 1687 (2000). Also see Bernd Heisele, Thomas Serre, and Stanley Bilesch, “Component-Based Approach to Face Detection,” Artificial Intelligence Laboratory and the Center for Biological and Computational Learning, MIT (2001), http://www.ai.mit.edu/research/abstracts/abstracts2001/vision-applied-to-people/03heisele2.pdf.
102. D. Van Essen and J. Gallant, “Neural Mechanisms of Form and Motion Processing in the Primate Visual System,” Neuron 13.1 (July 1994): 1–10.
103. Shimon Ullman, High-Level Vision: Object Recognition and Visual Cognition (Cambridge, Mass.: MIT Press, 1996); D. Mumford, “On the Computational Architecture of the Neocortex. II. The Role of Corticocortical Loops,” Biological Cybernetics 66.3 (1992): 241–51; R. Rao and D. Ballard, “Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex,” Neural Computation 9.4 (May 15, 1997): 721–63.
104. B. Roska and F. Werblin, “Vertical Interactions Across Ten Parallel, Stacked Representations in the Mammalian Retina,” Nature 410.6828 (March 29, 2001): 583–87; University of California, Berkeley, news release, “Eye Strips Images of All but Bare Essentials Before Sending Visual Information to Brain, UC Berkeley Research Shows,” March 28, 2001, www.berkeley.edu/news/media/releases/2001/03/28_ wers1.html.
105. Hans Moravec and Scott Friedman have founded a robotics company called See-grid based on Moravec’s research. See www.Seegrid.com.
106. M. A. Mahowald and C. Mead, “The Silicon Retina,” Scientific American 264.5 (May 1991): 76–82.
107. Specifically, a low-pass filter is applied to one receptor (such as a photoreceptor). This is multiplied by the signal of the neighboring receptor. If this is done in both directions and the results of each operation subtracted from zero, we get an output that reflects the direction of movement.
108. On Berger, see http://www.usc.edu/dept/engineering/CNE/faculty/Berger.html.
109. “The World’s First Brain Prosthesis,” New Scientist 177.2386 (March 15, 2003): 4, http://www.
newscientist.com/news/news.jsp?id=ns99993488.
110. Charles Choi, “Brain-Mimicking Circuits to Run Navy Robot,” UPI, June 7, 2004, http://www.upi.com/view.cfm?StoryID=20040606-103352-6086r.
111. Giacomo Rizzolatti et al., “Functional Organization of Inferior Area 6 in the Macaque Monkey. II. Area F5 and the Control of Distal Movements,” Experimental Brain Research 71.3 (1998): 491–507.
112. M. A. Arbib, “The Mirror System, Imitation, and the Evolution of Language,” in Kerstin Dautenhahn and Chrystopher L. Nehaniv, eds., Imitation in Animals and Artifacts (Cambridge, Mass.: MIT Press, 2002).
113. Marc D. Hauser, Noam Chomsky, and W. Tecumseh Fitch, “The Faculty of Language: What Is It, Who Has It, and How Did It Evolve?” Science 298 (November 2002): 1569–79, www.wjh.harvard.edu/~mnkylab/publications/languagespeech/Hauser,Chomsky,Fitch.pdf.
114. Daniel C. Dennett, Freedom Evolves (New York: Viking, 2003).
115. See Sandra Blakeslee, “Humanity? Maybe It’s All in the Wiring,” New York Times, December 11, 2003, http://www.nytimes.com/2003/12/09/science/09BRAI.html? ex=1386306000&en=294f5e91dd262a1a&ei=5007&partner=
USERLAND.
116. Antonio R. Damasio, Descartes’ Error: Emotion, Reason and the Human Brain (New York: Putnam, 1994).
117. M. P. Maher et al., “Microstructures for Studies of Cultured Neural Networks,” Medical and Biological Engineering and Computing 37.1 (January 1999): 110–18; John Wright et al., “Towards a Functional MEMS Neurowell by Physiological Experimentation,” Technical Digest, ASME, 1996 International Mechanical Engineering Congress and Exposition, Atlanta, November 1996, DSC (Dynamic Systems and Control Division), vol. 59, pp. 333–38.
118. W. French Anderson, “Genetics and Human Malleability,” Hastings Center Report 23.20 (January/February 1990): 1.
119. Ray Kurzweil, “A Wager on the Turing Test: Why I Think I Will Win,” KurzweilAI.net, April 9, 2002, http://www.KurzweilAI.net/meme/frame.html? main=/articles/art0374.html.
120. Robert A. Freitas Jr. proposes a future nanotechnology-based brain-uploading system that would effectively be instantaneous. According to Freitas (personal communication, January 2005), “An in vivo fiber network as proposed in http://www.nanomedicine.com/NMI/7.3.1.htm can handle 1018 bits/sec of data traffic, capacious enough for real-time brain-state monitoring. The fiber network has a 30 cm3 volume and generates 4–6 watts waste heat, both small enough for safe installation in a 1400 cm3 25-watt human brain. Signals travel at most a few meters at nearly the speed of light, so transit time from signal origination at neuron sites inside the brain to the external computer system mediating the upload are ~0.00001 msec which is considerably less than the minimum ~5 msec neuron discharge cycle time. Neuron-monitoring chemical sensors located on average ~2 microns apart can capture relevant chemical events occurring within a ~5 msec time window, since this is the approximate diffusion time for, say, a small neuropeptide across a 2-micron distance (http://www.nanomedicine.com/NMI/Tables/3.4.jpg). Thus human brain state monitoring can probably be instantaneous, at least on the timescale of human neural response, in the sense of ‘nothing of significance was missed.’ ”
121. M. C. Diamond et al., “On the Brain of a Scientist: Albert Einstein,” Experimental Neurology 88 (1985): 198–204.
Chapter Five: GNR: Three Overlapping Revolutions
1. Samuel Butler (1835–1902), “Darwin Among the Machines,” Christ Church Press, June 13, 1863 (republished by Festing Jones in 1912 in The Notebooks of Samuel Butler).
2. Peter Weibel, “Virtual Worlds: The Emperor’s New Bodies,” in Ars Electronica: Facing the Future, ed. Timothy Druckery (Cambridge, Mass.: MIT Press, 1999), pp. 207–23; available online at http://www.aec.at/en/archiv_files/19902/E1990b_009.pdf.
3. James Watson and Francis Crick, “Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic Acid,” Nature 171.4356 (April 23, 1953): 737–38, http://www.nature.com/nature/dna50/watsoncrick.pdf.
4. Robert Waterston quoted in “Scientists Reveal Complete Sequence of Human Genome,” CBC News, April 14, 2003, http://www.cbc.ca/story/science/national/
2003/04/14/genome030414.html.
5. See chapter 2, note 57.
6. The original reports of Crick and Watson, which still make compelling reading today, may be found in James A. Peters, ed., Classic Papers in Genetics (Englewood Cliffs, N.J.: Prentice-Hall, 1959). An exciting account of the successes and failures that led to the double helix is given in J. D. Watson, The Double Helix: A Personal Account of the Discovery of the Structure of DNA (New York: Atheneum, 1968). Nature.com has a collection of Crick’s papers available online at http://www. nature.com/nature/focus/crick/index.html.
7. Morislav Radman and Richard Wagner, “The High Fidelity of DNA Duplication,” Scientific American 259.2 (August 1988): 40–46.
8. The structure and behavior of DNA and RNA are described in Gary Felsenfeld, “DNA,” and James Darnell, “RNA,” both in Scientific American 253.4 (October 1985), p. 58–67 and 68–78 respectively.
9. Mark A. Jobling and Chris Tyler-Smith, “The Human Y Chromosome: An Evolutionary Marker Comes of Age,” Nature Reviews Genetics 4 (August 2003): 598–612; Helen Skaletsky et al., “The Male-Specific Region of the Human Y Chromosome Is a Mosaic of Discrete Sequence Classes,” Nature 423 (June 19, 2003): 825–37.
10. Misformed proteins are perhaps the most dangerous toxin of all. Research suggests that misfolded proteins may be at the heart of numerous disease processes in the body. Such diverse diseases as Alzheimer’s disease, Parkinson’s disease, the human form of mad-cow disease, cystic fibrosis, cataracts, and diabetes are all thought to result from the inability of the body to adequately eliminate misfolded proteins.
Protein molecules perform the lion’s share of cellular work. Proteins are made within each cell according to DNA blueprints. They begin as long strings of amino acids, which must then be folded into precise three-dimensional configurations in order to function as enzymes, transport proteins, et cetera. Heavy-metal toxins interfere with normal function of these enzymes, further exacerbating the problem. There are also genetic mutations that predispose individuals to mis-formed-protein buildup.
When protofibrils begin to stick together, they form filaments, fibrils, and ultimately larger globular structures called amyloid plaque. Until recently these accumulations of insoluble plaque were regarded as the pathologic agents for these diseases, but it is now known that the protofibrils themselves are the real problem. The speed with which a protofibril is turned into insoluble amyloid plaque is inversely related to disease progression. This explains why some individuals are found to have extensive accumulation of plaque in their brains but no evidence of Alzheimer’s disease, while others have little visible plaque yet extensive manifestations of the disease. Some people form amyloid plaque quickly, which protects them from further protofibril damage. Other individuals turn protofibrils into amyloid plaque less rapidly, allowing more extensive damage. These people also have little visible amyloid plaque. See Per Hammarström, Frank Schneider, and Jeffrey W. Kelly, “Trans-Suppression of Misfolding in an Amyloid Disease,” Science 293.5539 (September 28, 2001): 2459–62.
11. A fascinating account of the new biology is given in Horace F. Judson, The Eighth Day of Creation: The Makers of the Revolution in Biology (Woodbury, N.Y.: CSHL Press, 1996).
12. Raymond Kurzweil and Terry Grossman, M.D., Fantastic Voyage: Live Long Enough to Live Forever (New York: Rodale, 2004). See http://www.Fantastic-Voyage.net and http://www.RayandTerry.com.
13. Raymond Kurzweil, The 10% Solution for a Healthy Life: How to Eliminate Virtually All Risk of Heart Disease and Cancer (New York: Crown Books, 1993).
14. Kurzweil and Grossman, Fantastic Voyage. “Ray & Terry’s Longevity Program” is articulated throughout the book.
15. The test for “biological age,” called the H-scan test, includes tests for auditory-reaction time, highest audible pitch, vibrotactile sensitivity, visual-reaction time, muscle-movem
ent time, lung (forced expiratory) volume, visual-reaction time with decision, muscle-movement time with decision, memory (length of sequence), alternative button-tapping time, and visual accommodation. The author had this test done at Frontier Medical Institute (Grossman’s health and longevity clinic), http://www.FMIClinic.com. For information on the H-scan test, see Diagnostic and Lab Testing, Longevity Institute, Dallas, http://www.lidhealth.com/diagnostic.html.
16. Kurzweil and Grossman, Fantastic Voyage, chapter 10: “Ray’s Personal Program.”
17. Ibid.
18. Aubrey D. N. J. de Grey, “The Foreseeability of Real Anti-Aging Medicine: Focusing the Debate,” Experimental Gerontology 38.9 (September 2003): 927–34; Aubrey D. N. J. de Grey, “An Engineer’s Approach to the Development of Real Anti-Aging Medicine,” Science of Aging, Knowledge, Environment 1 (2003): Aubrey D. N. J. de Grey et al., “Is Human Aging Still Mysterious Enough to Be Left Only to Scientists?” BioEssays 24.7 (July 2002): 667–76.
19. Aubrey D. N. J. de Grey, ed., Strategies for Engineered Negligible Senescence: Why Genuine Control of Aging May Be Foreseeable, Annals of the New York Academy of Sciences, vol. 1019 (New York: New York Academy of Sciences, June 2004).
20. In addition to providing the functions of different types of cells, two other reasons for cells to control the expression of genes are environmental cues and developmental processes. Even simple organisms such as bacteria can turn on and off the synthesis of proteins depending on environmental cues. E. coli, for example, can turn off the synthesis of proteins that allow it to control the level of nitrogen gas from the air when there are other, less energy-intensive sources of nitrogen in its environment. A recent study of 1,800 strawberry genes found that the expression of 200 of those genes varied during different stages of development. E. Marshall, “An Array of Uses: Expression Patterns in Strawberries, Ebola, TB, and Mouse Cells,” Science 286.5439 (1999): 445.