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69. 69. K. Si et al., "A Neuronal Isoform of CPEB Regulates Local Protein Synthesis and Stabilizes Synapse-Specific Long-Term Facilitation in Aplysia," K. Si et al., "A Neuronal Isoform of CPEB Regulates Local Protein Synthesis and Stabilizes Synapse-Specific Long-Term Facilitation in Aplysia," Cell Cell 115.7 (December 26, 2003): 893904; K. Si, S. Lindquist, and E. R. Kandel, "A Neuronal Isoform of the Aplysia CPEB Has Prion-Like Properties," 115.7 (December 26, 2003): 893904; K. Si, S. Lindquist, and E. R. Kandel, "A Neuronal Isoform of the Aplysia CPEB Has Prion-Like Properties," Cell Cell 115.7 (December 26, 2003): 87991. These researchers have found that CPEB may help form and preserve long-term memories by undergoing shape changes in synapses similar to deformations of prions (protein fragments implicated in mad-cow disease and other neurologic illnesses). The study suggests that this protein does its good work while in a prion state, contradicting a widely held belief that a protein that has prion activity is toxic or at least doesn't function properly. This prion mechanism may also have roles in areas such as cancer maintenance and organ development, suspects Eric R. Kandel, University Professor of physiology and cell biophysics, psychiatry, biochemistry, and molecular biophysics at Columbia University and winner of a 2000 n.o.bel Prize for Medicine. See Whitehead Inst.i.tute press release, http://www.wi.mit.edu/nap/features/nap_feature_memory.html. 115.7 (December 26, 2003): 87991. These researchers have found that CPEB may help form and preserve long-term memories by undergoing shape changes in synapses similar to deformations of prions (protein fragments implicated in mad-cow disease and other neurologic illnesses). The study suggests that this protein does its good work while in a prion state, contradicting a widely held belief that a protein that has prion activity is toxic or at least doesn't function properly. This prion mechanism may also have roles in areas such as cancer maintenance and organ development, suspects Eric R. Kandel, University Professor of physiology and cell biophysics, psychiatry, biochemistry, and molecular biophysics at Columbia University and winner of a 2000 n.o.bel Prize for Medicine. See Whitehead Inst.i.tute press release, http://www.wi.mit.edu/nap/features/nap_feature_memory.html.

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78. 78. J. Voogd and M. Glickstein, "The Anatomy of the Cerebellum," J. Voogd and M. Glickstein, "The Anatomy of the Cerebellum," Trends in Neuroscience Trends in Neuroscience 21.9 (September 1998): 37075; John C. Eccles, Masao Ito, and Janos Szentagothai, 21.9 (September 1998): 37075; John C. Eccles, Masao Ito, and Janos Szentagothai, The Cerebellum as a Neuronal Machine The Cerebellum as a Neuronal Machine (New York: Springer-Verlag, 1967); Masao Ito, (New York: Springer-Verlag, 1967); Masao Ito, The Cerebellum and Neural Control The Cerebellum and Neural Control (New York: Raven, 1984). (New York: Raven, 1984).

79. 79. N. Bernstein, N. Bernstein, The Coordination and Regulation of Movements The Coordination and Regulation of Movements (New York: Pergamon Press, 1967). (New York: Pergamon Press, 1967).

80. 80. U. S. Office of Naval Research press release, "Boneless, Brainy, and Ancient," September 26, 2001, http://www.eurekalert.org/pub_releases/2001-11/oonr-bba112601.php; the octopus arm "could very well be the basis of next-generation robotic arms for undersea, s.p.a.ce, as well as terrestrial applications." U. S. Office of Naval Research press release, "Boneless, Brainy, and Ancient," September 26, 2001, http://www.eurekalert.org/pub_releases/2001-11/oonr-bba112601.php; the octopus arm "could very well be the basis of next-generation robotic arms for undersea, s.p.a.ce, as well as terrestrial applications."

81. 81. S. Grossberg and R.W. Paine, "ANeural Model of Cortico-Cerebellar Interactions During Attentive Imitation and Predictive Learning of Sequential Handwriting Movements," Neural Networks 13.8-9 (October-November 2000): 999-1046. S. Grossberg and R.W. Paine, "ANeural Model of Cortico-Cerebellar Interactions During Attentive Imitation and Predictive Learning of Sequential Handwriting Movements," Neural Networks 13.8-9 (October-November 2000): 999-1046.

82. 82. Voogd and Glickstein, "Anatomy of the Cerebellum"; Eccles, Ito, and Szentagothai, Voogd and Glickstein, "Anatomy of the Cerebellum"; Eccles, Ito, and Szentagothai, Cerebellum as a Neuronal Machine Cerebellum as a Neuronal Machine; Ito, Cerebellum and Neural Control Cerebellum and Neural Control; R. Llinas, in Handbook of Physiology Handbook of Physiology, vol. 2, The Nervous System The Nervous System, ed. V.B. Brooks (Bethesda, Md.: American Physiological Society, 1981), pp. 831976.

83. 83. J. L. Raymond, S. G. Lisberger, and M. D. Mauk, "The Cerebellum: A Neuronal Learning Machine?" J. L. Raymond, S. G. Lisberger, and M. D. Mauk, "The Cerebellum: A Neuronal Learning Machine?" Science Science 272.5265 (May 24, 1996): 112631; J. J. Kim and R. F. Thompson, "Cerebellar Circuits and Synaptic Mechanisms Involved in Cla.s.sical Eyeblink Conditioning," 272.5265 (May 24, 1996): 112631; J. J. Kim and R. F. Thompson, "Cerebellar Circuits and Synaptic Mechanisms Involved in Cla.s.sical Eyeblink Conditioning," Trends in Neuroscience Trends in Neuroscience 20.4 (April 1997): 17781. 20.4 (April 1997): 17781.

84. 84. The simulation included 10,000 granule cells, 900 Golgi cells, 500 mossy fiber cells, 20 Purkinje cells, and 6 nucleus cells. The simulation included 10,000 granule cells, 900 Golgi cells, 500 mossy fiber cells, 20 Purkinje cells, and 6 nucleus cells.

85. 85. J. F. Medina et al., "Timing Mechanisms in the Cerebellum: Testing Predictions of a Large-Scale Computer Simulation," J. F. Medina et al., "Timing Mechanisms in the Cerebellum: Testing Predictions of a Large-Scale Computer Simulation," Journal of Neuroscience Journal of Neuroscience 20.14 (July 15, 2000): 551625; Dean Buonomano and Michael Mauk, "Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Reponses," 20.14 (July 15, 2000): 551625; Dean Buonomano and Michael Mauk, "Neural Network Model of the Cerebellum: Temporal Discrimination and the Timing of Motor Reponses," Neural Computation Neural Computation 6.1 (1994): 3855. 6.1 (1994): 3855.

86. 86. Medina et al., "Timing Mechanisms in the Cerebellum." Medina et al., "Timing Mechanisms in the Cerebellum."

87. 87. Carver Mead, Carver Mead, a.n.a.log VLSI and Neural Systems a.n.a.log VLSI and Neural Systems (Boston: Addison-Wesley Longman, 1989). (Boston: Addison-Wesley Longman, 1989).

88. 88. Lloyd Watts, "Visualizing Complexity in the Brain," in Lloyd Watts, "Visualizing Complexity in the Brain," in Computational Intelligence: The Experts Speak Computational Intelligence: The Experts Speak, D. Fogel and C. Robinson, eds. (Hoboken, N.J.: IEEE Press/Wiley, 2003), pp. 4556, http://www.lloydwatts.com/wcci.pdf.

89. 89. Ibid. Ibid.

90. 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. 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. 91. See http://info.med.yale.edu/bbs/faculty/she_go.html. See http://info.med.yale.edu/bbs/faculty/she_go.html.

92. 92. Gordon M. Shepherd, ed., Gordon M. Shepherd, ed., The Synaptic Organization of the Brain The Synaptic Organization of the Brain, 4th ed. (New York: Oxford University Press, 1998), p. vi.

93. 93. E.Young, "Cochlear Nucleus," in ibid., pp. 12158. E.Young, "Cochlear Nucleus," in ibid., pp. 12158.

94. 94. Tom Yin, "Neural Mechanisms of Encoding Binaural Localization Cues in the Auditory Brainstem," in D. Oertel, R. Fay, and A. Popper, eds., 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 Integrative Functions in the Mammalian Auditory Pathway (New York: Springer-Verlag, 2002), pp. 99159. (New York: Springer-Verlag, 2002), pp. 99159.

95. 95. John Ca.s.seday, Thane Premouw, and Ellen Covey, "The Inferior Colliculus: A Hub for the Central Auditory System," in Oertel, Fay, and Popper, John Ca.s.seday, Thane Premouw, 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 Integrative Functions in the Mammalian Auditory Pathway, pp. 238318.

96. 96. Diagram by Lloyd Watts, http://www.lloydwatts.com/neuroscience.shtml, adapted from E.Young, "Cochlear Nucleus" in G. Shepherd, ed., 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 The Synaptic Organization of the Brain, 4th ed. (New York: Oxford University Press, 2003 [first published 1998]), pp. 12158; D. Oertel in D. Oertel, R. Fay, and A. Popper, eds., Integrative Functions in the Mammalian Auditory Pathway Integrative Functions in the Mammalian Auditory Pathway (New York: Springer-Verlag, 2002), pp. 15; John Ca.s.seday, T. Fremouw, and E. Covey, "Inferior Colliculus" in ibid.; J. LeDoux, (New York: Springer-Verlag, 2002), pp. 15; John Ca.s.seday, T. Fremouw, and E. Covey, "Inferior Colliculus" in ibid.; J. LeDoux, The Emotional Brain 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," (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 Proceedings of the National Academy of Sciences 97.22: 1180011806. 97.22: 1180011806.

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 0C.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 a.s.sociated with emotion, memory, territory, et cetera.AC: Auditory cortex.

97. 97. M. S. Humayun et al., "Human Neural Retinal Transplantation," M. S. Humayun et al., "Human Neural Retinal Transplantation," Investigative Ophthalmology and Visual Science Investigative Ophthalmology and Visual Science 41.10 (September 2000): 31003106. 41.10 (September 2000): 31003106.

98. 98. Information Science and Technology Colloquium Series, May 23, 2001, http://isandtcolloq.gsfc.nasa.gov/spring2001/speakers/poggio.html. Information Science and Technology Colloquium Series, May 23, 2001, http://isandtcolloq.gsfc.nasa.gov/spring2001/speakers/poggio.html.

99. 99. Kah-Kay Sung and Tomaso Poggio, "Example-Based Learning for View-Based Human Face Detection," Kah-Kay Sung and Tomaso Poggio, "Example-Based Learning for View-Based Human Face Detection," IEEE Transactions on Pattern a.n.a.lysis and Machine Intelligence IEEE Transactions on Pattern a.n.a.lysis and Machine Intelligence 20.1 (1998): 3951, http://portal.acm.org/citation.cfm?id=275345&dl= ACM&coll=GUIDE. 20.1 (1998): 3951, http://portal.acm.org/citation.cfm?id=275345&dl= ACM&coll=GUIDE.

100. 100. Maximilian Riesenhuber and Tomaso Poggio, "A Note on Object Cla.s.s 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. Maximilian Riesenhuber and Tomaso Poggio, "A Note on Object Cla.s.s 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. 101. K. Tanaka, "Inferoternporal Cortex and Object Vision," K. Tanaka, "Inferoternporal Cortex and Object Vision," Annual Review of Neuroscience Annual Review of Neuroscience 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-publicationszSz15001999zSzAIM-1664.pdf/mohan99object.pdf; Anuj Mohan, Constantine Papageorgiou, and Tomaso Poggio, "Example-Based Object Detection in Images by Components," 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-publicationszSz15001999zSzAIM-1664.pdf/mohan99object.pdf; Anuj Mohan, Constantine Papageorgiou, and Tomaso Poggio, "Example-Based Object Detection in Images by Components," IEEE Transactions on Pattern a.n.a.lysis and Machine Intelligence IEEE Transactions on Pattern a.n.a.lysis and Machine Intelligence 23.4 (April 2001), http://cbcl.mit.edu/projects/cbd/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.edulresearch/abstracts/abstracts2001/vision-applied-to-people/03heisele2.pdf. 23.4 (April 2001), http://cbcl.mit.edu/projects/cbd/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.edulresearch/abstracts/abstracts2001/vision-applied-to-people/03heisele2.pdf.

102. 102. D.Van Essen and J. Gallant, "Neural Mechanisms of Form and Motion Processing in the Primate Visual System," D.Van Essen and J. Gallant, "Neural Mechanisms of Form and Motion Processing in the Primate Visual System," Neuron Neuron 13.1 (July 1994): 110. 13.1 (July 1994): 110.

103. 103. Shimon Ullman, Shimon Ullman, High-Level Vision: Object Recognition and Visual Cognition High-Level Vision: Object Recognition and Visual Cognition (Cambridge, Ma.s.s.: MIT Press, 1996); D. Mumford, "On the Computational Architecture of the Neocortex. II. The Role of Corticocortical Loops," (Cambridge, Ma.s.s.: MIT Press, 1996); D. Mumford, "On the Computational Architecture of the Neocortex. II. The Role of Corticocortical Loops," Biological Cybernetics Biological Cybernetics 66.3 (1992): 24151; R. Rao and D. Ballard, "Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex," 66.3 (1992): 24151; R. Rao and D. Ballard, "Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex," Neural Computation Neural Computation 9.4 (May 15, 1997): 72163. 9.4 (May 15, 1997): 72163.

104. 104. B. Roska and F.Werblin, "Vertical Interactions Across Ten Parallel, Stacked Representations in the Mammalian Retina," B. Roska and F.Werblin, "Vertical Interactions Across Ten Parallel, Stacked Representations in the Mammalian Retina," Nature Nature 410.6828 (March 29, 2001): 58387; 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/200l/03/28_wers1.html. 410.6828 (March 29, 2001): 58387; 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/200l/03/28_wers1.html.

105. 105. Hans Moravec and Scott Friedman have founded a robotics company called Seegrid based on Moravec's research. See www.Seegrid.com. Hans Moravec and Scott Friedman have founded a robotics company called Seegrid based on Moravec's research. See www.Seegrid.com.

106. 106. M. A. Mahowald and C. Mead, "The Silicon Retina," M. A. Mahowald and C. Mead, "The Silicon Retina," Scientific American Scientific American 264.5 (May 1991): 7682. 264.5 (May 1991): 7682.

107. 107. Specifically, a low-pa.s.s 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. Specifically, a low-pa.s.s 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. 108. On Berger, see http://www.usc.edu/dept/engineering/CNE/faculty/Berger.html. On Berger, see http://www.usc.edu/dept/engineering/CNE/faculty/Berger.html.

109. 109. "The World's First Brain Prosthesis," "The World's First Brain Prosthesis," New Scientist New Scientist 177.2386 (March 15,2003): 4, http://www.newscientist.com/news/news.jsp?id=ns99993488. 177.2386 (March 15,2003): 4, http://www.newscientist.com/news/news.jsp?id=ns99993488.

110. 110. Charles Choi, "Brain-Mimicking Circuits to Run Navy Robot," UPI, June 7, 2004, http://www.upi.com/view.cfm?StoryID=20040606-103352-6086r. Charles Choi, "Brain-Mimicking Circuits to Run Navy Robot," UPI, June 7, 2004, http://www.upi.com/view.cfm?StoryID=20040606-103352-6086r.

111. 111. Giacomo Rizzolatti et al., "Functional Organization of Inferior Area 6 in the Macaque Monkey. II. Area F5 and the Control of Distal Movements," 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 Experimental Brain Research 71.3 (1998): 491507. 71.3 (1998): 491507.

112. 112. M. A. Arbib, "The Mirror System, Imitation, and the Evolution of Language," in Kerstin Dautenhahn and Chrystopher L. Nehaniv, eds., 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 Imitation in Animals and Artifacts (Cambridge, Ma.s.s.: MIT Press, 2002). (Cambridge, Ma.s.s.: MIT Press, 2002).

113. 113. Marc D. Hauser, Noam Chomsky, and W. Tec.u.mseh Fitch, "The Faculty of language: What Is It, Who Has It, and How Did It Evolve?" Marc D. Hauser, Noam Chomsky, and W. Tec.u.mseh Fitch, "The Faculty of language: What Is It, Who Has It, and How Did It Evolve?" Science Science 298 (November 2002): 156979, www.wjh.harvard.edu/~mnkylab/publications/languagespeech/Hauser,Chomsky,Fitch.pdf. 298 (November 2002): 156979, www.wjh.harvard.edu/~mnkylab/publications/languagespeech/Hauser,Chomsky,Fitch.pdf.

114. 114. Daniel C. Dennett, Daniel C. Dennett, Freedom Evolves Freedom Evolves (New York: Viking, 2003). (New York: Viking, 2003).

115. 115. See Sandra Blakeslee, "Humanity? Maybe It's All in the Wiring," See Sandra Blakeslee, "Humanity? Maybe It's All in the Wiring," New York Times New York Times, December 11, 2003, http://www.nytimes.com/2003112/09/science/09BRAI.html?ex=1386306000&en=294f5e91dd262a1a&ei=5007&partner=USERLAND.

116. 116. Antonio R. Damasio, Antonio R. Damasio, Descartes' Error: Emotion, Reason and the Human Brain Descartes' Error: Emotion, Reason and the Human Brain (New York: Putnam, 1994). (New York: Putnam, 1994).

117. 117. M. P. Maher et al., "Microstructures for Studies of Cultured Neural Networks," M. P. Maher et al., "Microstructures for Studies of Cultured Neural Networks," Medical and Biological Engineering and Computing Medical and Biological Engineering and Computing 37.1 (January 1999): 11018; John Wright et al., "Towards a Functional MEMS Neurowell by Physiological Experimentation," 37.1 (January 1999): 11018; John Wright et al., "Towards a Functional MEMS Neurowell by Physiological Experimentation," Technical Digest Technical Digest, ASME, 1996 International Mechanical Engineering Congress and Exposition, Atlanta, November 1996, DSC (Dynamic Systems and Control Division), vol. 59, pp. 33338.

118. 118. W. French Anderson, "Genetics and Human Malleability," W. French Anderson, "Genetics and Human Malleability," Hastings Center Report Hastings Center Report 23.20 (January/February 1990): 1. 23.20 (January/February 1990): 1.

119. 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. 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. 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 10 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 cm bits/sec of data traffic, capacious enough for real-time brain-state monitoring. The fiber network has a 30 cm3 volume and generates 46 watts waste heat, both small enough for safe installation in a 1400 cm volume and generates 46 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/NMII/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.' " 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/NMII/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. 121. M. C. Diamond et al., "On the Brain of a Scientist: Albert Einstein," M. C. Diamond et al., "On the Brain of a Scientist: Albert Einstein," Experimental Neurology Experimental Neurology 88 (1985): 198204. 88 (1985): 198204.