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68.
S. Lowel and W. Singer, “Selection of Intrinsic Horizontal Connections in the Visual Cortex by Correlated Neuronal Activity,”
Science
255.5041 (January 10, 1992): 209–12.

69.
K. Si et al., “A Neuronal Isoform of CPEB Regulates Local Protein Synthesis and Stabilizes Synapse-Specific Long-Term Facilitation in Aplysia,”
Cell
115.7 (December 26, 2003): 893–904; K. Si, S. Lindquist, and E. R. Kandel, “A Neuronal Isoform of the Aplysia CPEB Has Prion-Like Properties,”
Cell
115.7 (December 26, 2003): 879–91. 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 Nobel Prize for Medicine. See Whitehead Institute press release,
http://www.wi.mit.edu/nap/features/nap_feature_memory.html
.

70.
M. C. Anderson et al., “Neural Systems Underlying the Suppression of Unwanted Memories,”
Science
303.5655 (January 9, 2004): 232–35. The findings could encourage the development of new ways for people to overcome traumatizing memories. Keay Davidson, “Study Suggests Brain Is Built to Forget: MRIs in Stanford Experiments Indicate Active Suppression of Unneeded Memories,”
San Francisco Chronicle
, January 9, 2004,
http://www.sfgate.com/cgi-bin/article.cgi?file=/c/a/2004/01/09/FORGET.TMP&type=science
.

71.
Dieter C. Lie et al., “Neurogenesis in the Adult Brain: New Strategies for CNS Diseases,”
Annual Review of Pharmacology and Toxicology
44 (2004): 399–421.

72.
H. van Praag, G. Kempermann, and F. H. Gage, “Running Increases Cell Proliferation and Neurogenesis in the Adult Mouse Dentate Gyrus,”
Nature Neuroscience
2.3 (March 1999): 266–70.

73.
Minsky and Papert,
Perceptrons
.

74.
Ray Kurzweil,
The Age of Spiritual Machines
(New York: Viking, 1999), p. 79.

75.
Basis functions are nonlinear functions that can be combined linearly (by adding together multiple weighted-basis functions) to approximate any nonlinear function. Pouget and Snyder, “Computational Approaches to Sensorimotor Transformations,”
Nature Neuroscience
3.11 Supplement (November 2000): 1192–98.

76.
T. Poggio, “A Theory of How the Brain Might Work,” in
Proceedings of Cold Spring Harbor Symposia on Quantitative Biology
4 (Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 1990), 899–910. Also see T. Poggio and E. Bizzi, “Generalization in Vision and Motor Control,”
Nature
431 (2004): 768–74.

77.
R. Llinas and J. P. Welsh, “On the Cerebellum and Motor Learning,”
Current Opinion in Neurobiology
3.6 (December 1993): 958–65; E. Courchesne and G. Allen, “Prediction and Preparation, Fundamental Functions of the Cerebellum,”
Learning and Memory
4.1 (May–June 1997): 1–35; J. M. Bower, “Control of Sensory Data Acquisition,”
International Review of Neurobiology
41 (1997): 489–513.

78.
J. Voogd and M. Glickstein, “The Anatomy of the Cerebellum,”
Trends in Neuro-science
21.9 (September 1998): 370–75; John C. Eccles, Masao Ito, and János Szentágothai,
The Cerebellum as a Neuronal Machine
(New York: Springer-Verlag, 1967); Masao Ito,
The Cerebellum and Neural Control
(New York: Raven, 1984).

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

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-bba112 601.php
; the octopus arm “could very well be the basis of next-generation robotic arms for undersea, space, as well as terrestrial applications.”

81.
S. Grossberg and R. W. Paine, “A Neural 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.
Voogd and Glickstein,“Anatomy of the Cerebellum”; Eccles, Ito, and Szentágothai,
Cerebellum as a Neuronal Machine
; Ito,
Cerebellum and Neural Control
; R. Llinas, in
Handbook of Physiology
, vol. 2,
The Nervous System
, ed. V. B. Brooks (Bethesda, Md.: American Physiological Society, 1981), pp. 831–976.

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
.

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