• Karen A. Moxon
Part of the Bioelectric Engineering book series (BEEG)


Images from Hollywood suggest that by directly communicating with the brain it may be possible to control human behavior (Terminal Man) or provide a new reality far more interesting than what we currently experience (The Matrix). Unfortunately, Hollywood has always been a bit ahead of science and our ability to directly interface with the brain is at its infancy. There are, however, some clear examples of successful neural prosthetic devices that suggest the possibility of restoring function after injury. For example, over 30,000 auditory prostheses have been successfully implanted in patients with sensorineural hearing loss (Rubenstein and Miller, 1999). These devices bypass normal signaling mechanisms in the ear by translating sounds into patterns of stimulation and directly activate nerve cells to improve hearing in a broad range of patients. Another example of successful neural prosthetics is the technique for electrically stimulating either the muscles or nerves that innervate them to restore some function after paralysis. Over 150 functional electrical stimulation (FES) devices have been implanted into patients. These devices have been used to assist in breathing, bladder control, posture, and locomotion. There are now commercially available neural prosthetic devices (Smith et ai, 1987; Peckham et al, 2000) that restore hand grasp function by stimulating muscles through electrodes. The electrodes are controlled by movement of the shoulder or neck and they stimulate nerves in the arm or wrist to restore grasping function in patients who have suffered loss of function in their arms or hands.


Single Neuron Limb Movement Recording Site Neural Signal Spike Time 
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  1. Abeles, 1991, Corticonics, Academic Press, Boston, MA.Google Scholar
  2. Bai, Q., and Wise, K. D., 2001, Single-unit neural recording with active microelectrode arrays, IEEE Trans. Biomed. Eng. 48(8):911–920.CrossRefGoogle Scholar
  3. Bai, Q., Wise, K. D., and Anderson, D. J., 2000, A high-yield microassembly structure for three-dimensional microelectrode arrays, IEEE Trans. Biomed. Eng. 47(3):281–289.CrossRefGoogle Scholar
  4. Bakoglu, H., Baldwin, G., Li, Z., Tsai, C., and Zhang, J., 1990, Circuits, Interconnections and Packaging for VLSI, Addison-Wesley, Boston, MA.Google Scholar
  5. Bar-Gad, I., Ritov, Y., Vaadia, E., and Bergman, H., 2001, Failure in identification of overlapping spikes from multiple neuron activity causes artificial correlations, J. Neurosci. Methods 107(1–2):1–13.CrossRefGoogle Scholar
  6. Bement, S. L., Wise, K. D., Anderson, D. J., Najafi, K., Drake, K. L., 1986, Solid-state electrodes for multichannel multiplexed intracortical neuronal recording, IEEE Trans. Biomed. Eng. 33(2):230–240.CrossRefGoogle Scholar
  7. Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kubler, A., Perelmouter, J., Taub, E., and Flor, H., 1999, A spelling device for the paralysed, Nature 398(6725):297–298.CrossRefGoogle Scholar
  8. Blum, N. A., Carkhuff, B. G., Charles, H. K., Edwards, R. L., and Meyer, R. A., 1991, Multisite microprobes for neural recordings, IEEE Trans. Biomed. Eng. 38(1):68.CrossRefGoogle Scholar
  9. Bragin, J., Hetke, C. L., Wilson, D. J., Anderson, J. E., Jr, and Buzsaki, G., 2000, Multiple site silicon-based probes for chronic recordings in freely moving rats: Implantation, recording and histological verification, J. Neurosci. Methods 98:77–82.CrossRefGoogle Scholar
  10. Carmena, J. M., Lebedev, M. A., Crist, R. E., O’Doherty, E., Scatucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., and Micolelis, M. A. L., 2003, Learning to control a brain-machine interface for reaching and grasping by primates, PLOS Biol. 1(2):1–16.CrossRefGoogle Scholar
  11. Carter, R., and Houk, J. C., 1993, Multiple single-unit recordings from the CNS using thin-film electrode arrays, IEEE Trans. Rehabil. Eng. 1:3–18.CrossRefGoogle Scholar
  12. Chapin, J. K., Moxon, K. A., Markowitz, R. S., and Nicolelis, M. A. L., 1999, Realtime control of a robot arm using simultaneously recorded neurons, Nat. Neurosci. 2(7):1–7.CrossRefGoogle Scholar
  13. Chapin, J. K., and Nicolelis, M. A. L., 2000, Brain control of sensorimotor prosthesis, In: Neural Prostheses for Restoration of Sensory and Motor Function (J. K. Chapin and K. A. Moxon, eds.), CRC Press, Boca Raton, pp. 45–74.Google Scholar
  14. Chapin, J. K., and Nicolelis, M. A. L., 1999, Principal component analysis of neuronal ensemble activity reveals multidimensional somatosensory representations, J. Neurosci. Methods 94:121–140.CrossRefGoogle Scholar
  15. Donoghue, J. P., 2002, Connecting cortex to machines: Recent advances in brain interfaces, Nat. Neurosci. 4(Suppl.):1085–1088.CrossRefGoogle Scholar
  16. Drake, K. L., Wise, K. D., Farraye, J., Anderson, D. J., and Bement, S. L., 1988, Performance of planar multisite microarrays in recording extracellular single-unit intracortical activity, IEEE Trans. Biomed. Eng. 35:719–732.CrossRefGoogle Scholar
  17. Eccles, J. C., 1981, The modular operation of the cerebral neocortex considered as the material basis of mental events, Neuroscience 6:1839–1859.CrossRefGoogle Scholar
  18. Eggermont, J. J., 1993, Functional aspects of synchrony and correlation in the auditory nervous system, Concepts Neurosci. 4:105.Google Scholar
  19. Eichman, H., and Kuperstein, M., 1986, Extracellular neural recording with multichannel microelectrodes, J. Electrophysiol. Tech. 13:189.Google Scholar
  20. Evarts, E. V., 1974, Precentral and postcentral cortical activity in association with visually triggered movement, J. Neurophysiol. 37(2):373.Google Scholar
  21. Foffani, G., and Moxon, K. A., 2004, PSTH-based classification of sensory stimuli, J. Neurosci. Methods 135:107–120.CrossRefGoogle Scholar
  22. Freeman, W. J., 1983, The physiological basis of mental images, Biol. Psych. 18:1107–1125.Google Scholar
  23. Gerstein, G. L., Perkel, D. H., and Subramanian, K. N., 1978, Identification of functionally related neural assemblies, Brain Res. 140:43–62.CrossRefGoogle Scholar
  24. Gerstein, G. L., and Perkel, D. H., 1969, Simultaneously recorded trains of action potentials: Analysis and functional interpretation, Science 164(881):828.CrossRefGoogle Scholar
  25. Georgopoulos, A. P., Schwartz, A. B., and Kettner, R. E., 1986, Neuronal population coding of movement direction, Science 233:1416–1419.CrossRefGoogle Scholar
  26. Ghazanfar, A. A., Stambaugh, C. R., and Nicolelis, M. A., 2000, Encoding of tactile stimulus location by somatosensory thalamocortical ensembles, J. Neurosci. 20:3761–3775.Google Scholar
  27. Gray, P. R., and Meyer, R. G., 1993, Analysis and Design of Analog Integrated Circuits, John Wiley & Sons, Inc., New York.Google Scholar
  28. Guillory, K. S., and Normann, R. A., 1999, A 100-channel system for real time detection and storage of extracellular spike waveforms, J. Neurosci. Methods 91:21–29.CrossRefGoogle Scholar
  29. Hebb, D. O., 1949, Organization of Behavior, McGraw-Hill, New York.Google Scholar
  30. Hoogerwerf, A. C., and Wise, K. D., 1994, A three-dimensional microelectrode array for chronic neural recording, IEEE Trans. Biomed. Eng. 41:1136–1146.CrossRefGoogle Scholar
  31. Hopfield, J. J., and Herz, A. V., 1995, Rapid local synchronization of action potentials: Toward computation with coupled integrate-and-fire neurons, Proc. Natl. Acad. Sci. U.S.A. 92:6655–6662.CrossRefGoogle Scholar
  32. Hopfield, J. J., 1995, Pattern recognition computation using action potential timing for stimulus representation, Nature 376:33–36.CrossRefGoogle Scholar
  33. Hubel, D. H., and Wiesel, T. N., 1959, Receptive fields of single neurones in the cat’s striate cortex, J. Physiol. 148:574–591.Google Scholar
  34. Humphrey, D. R., 1970, A chronically implantable multiple micro-electrode system with independent control of electrode position, Electroencephal. Clin. Neurophysiol. 29:616.CrossRefGoogle Scholar
  35. Humphrey, D. R., Schmidt, E. M., and Thompson, W. D., 1970, Predicting measures of motor performance from multiple cortical spike trains, Science 170(959):759.CrossRefGoogle Scholar
  36. Hulata, E., Segev, R., and Ben-Jacob, E., 2002, A method for spike sorting and detection based on wavelet packets and Shannon’s mutual information, J. Neurosci. Methods 117(1):1–12.CrossRefGoogle Scholar
  37. Jack, J. J., Noble, B. D., and Tsien, R. W., 1975, Electric Current Flow in Excitable Cells, Oxford University Press.Google Scholar
  38. Jackson, J. E., 1991, A User’s Guide to Principal Components, John Wiley and Sons, Inc., New York, pp. 1–25.zbMATHGoogle Scholar
  39. John, E. R., 1972, Switchboard versus statistical theories of learning and memory, Science 177:850–864.CrossRefGoogle Scholar
  40. Jin, J., and Wise, K. D., 1992, An implantable CMOS circuit interface for multiplexed microelectrode recording arrays, IEEE Trans. Biomed. Eng. 27(3):433–443.Google Scholar
  41. Kennedy, P. R., and Bakay, R. A. E., 1998, Restoration of neural output from a paralyzed subject by a direct brain connection, NeuroReport 9:1707.CrossRefGoogle Scholar
  42. Kennedy, P. R., et al., 2000, Direct control of a computer from the human central nervous system, IEEE Trans. Rehabil. Eng. 8:198–202.CrossRefGoogle Scholar
  43. Kennedy, P. R., 1989, The cone electrode: A long-term electrode that records from neurites grown onto its recording surface, J. Neurosci. Methods 29:181–193.CrossRefGoogle Scholar
  44. Kennedy, P. R., Bakay, R. A. E., and Sharpe, S. M., 1992, Behavioral correlates of action potentials recorded chronically inside the cone electrodes, Neuroreport 2:605.CrossRefGoogle Scholar
  45. Kennedy, P. R., and King, B., 2000, Dynamic interplay of neural signals during the emergence of cursor related cortex in a human implanted with the neurotrophic electrode, In: Neural Prostheses for Restoration of Sensory and Motor Function (J. K. Chapin and K. A. Moxon, eds.), CRC Press, Boca Raton, pp. 45–74.Google Scholar
  46. Kim, K. H., and Kim, S. J., 2000, Noise performance design of CMOS preamplifier for the active semiconductor neural probe, IEEE Trans. Biomed. Eng. 47(8):1097–1105.CrossRefGoogle Scholar
  47. Kim, K. H., and Kim, S. J., 2003, Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio, IEEE Trans. Biomed. Eng. 50(4):421–431.CrossRefGoogle Scholar
  48. Kralik, J. D., Dimitrov, D. F., Krupa, D. J., Katz, D. B., Cohen, D., and Nicolelis, M. A., 2001, Techniques for long-term multisite neuronal ensemble recordings in behaving animals, Methods 25(2):121–150.CrossRefGoogle Scholar
  49. Kreiter, A. K., Aertsen, A. M., and Gerstein, G. L., 1989, A low-cost single-board solution for real-time, unsupervised waveform classification of multineuron recordings, J. Neurosci. Methods 30(1):59–69.Google Scholar
  50. Kubie, L. S., 1930, A theoretical application to some neurological problems of the properties of excitation waves which move in closed circuits, Brain 53:166–177.CrossRefGoogle Scholar
  51. Lashley, K. S., 1950, In search of the engram, Symp. Soc. Exp. Biol. 4:454–482.Google Scholar
  52. Letelier, J. C., and Weber, P. P., 2000, Spike sorting based on discrete wavelet transform coefficients, J. Neurosci. Methods 101(2):93–106.CrossRefGoogle Scholar
  53. Lewicki, M. S., 1998, A review of methods for spike sorting: The detection and classification of neural action potentials, Network 9(4):R53–R78.zbMATHMathSciNetGoogle Scholar
  54. Lin, Y., Tsai, C., Huang, H., Chiou, D., and Wu, C., 1999, Preamplifier with a second-order high-pass filtering characteristic, IEEE Trans. Biomed. Eng. 46:609–612.CrossRefGoogle Scholar
  55. Liu, X., McCreery, D. B., Carter, R. R., Bullara, L. A., Yeun, T. G. H., and Agnew, W. F., 1999, Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes, IEEE Trans. Rehabil. Eng. 7:315.CrossRefGoogle Scholar
  56. MacGregor, R. J., 1991, Sequential configuration model for firing patterns in local neural networks, Biol. Cyber. 65:339–349.CrossRefGoogle Scholar
  57. MacGregor, R., 1993, Theoretical Mechanics of Biological Neural Networks, Academic Press, Boston.zbMATHGoogle Scholar
  58. Middendorf, M., McMillan, G., Calhoun, G., and Jones, K. S., 2000, Brain-computer interfaces based on steadystate visual evoked response, IEEE Trans. Rehabil. Eng. 8:211–213.CrossRefGoogle Scholar
  59. Mohseni, P., and Najafi, K., 2004, A fully integrated neural recording amplifier with DC input stabilization, IEEE Trans. Biomed. Eng. 51(5):832–837.CrossRefGoogle Scholar
  60. Moxon, K. A., 1999, Multichannel electrode design: Considerations for different applications, In: Methods for Simultaneous Neuronal Ensemble Recordings (M. A. L. Nicolelis, eds.), CRC Press, Boca Raton, FL, pp. 25–45.Google Scholar
  61. Moxon, K. A., Gerhardt, G. A., Bickford, P. C., Rose, G. M., Woodward, D. J., and Adler, L. E., 1999, Multiple single units and populations responses during inhibitory gating of hippocampal auditory response in freely-moving rats, Brain Res. 825:75–85.CrossRefGoogle Scholar
  62. Moxon, K. A., Kalkhoran, N. M., Markert, M. A., Sambito, M. A., McKenzie, J. L., and Webster, J. T., 2004b, Nanostructured surface modification of microelectrodes to enhance biocompatibility for a direct brain machine interface, IEEE Trans. Biomed. Eng. 1(6):881–889.CrossRefGoogle Scholar
  63. Moxon, K. A., Leiser, S. C., Gerhardt, G. A., Barbee, K., and Chapin, J. K., 2004a, Ceramic based multisite electrode arrays for electrode recording, IEEE Trans. Biomed. Eng. 51(4):647–656.CrossRefGoogle Scholar
  64. Moxon, K. A., Morizio, J., Chapin, J. K., Nicolelis, M. A. L., and Wolf, P. D., 2000, Designing a brain-machine interface for neuroprosthetic control, In: Neural Prostheses for Restoration of Sensory and Motor Function (J. K. Chapin and K. A. Moxon, eds.), CRC Press, Boca Raton, pp. 45–74.Google Scholar
  65. Moxon, K. A., Gerhardt, G. A., Gulinello, M., and Adler, L. E., 2003a, Inhibitory control of sensory gating in a computer model of the CA3 region of the hippocampus, Biol. Cyber. 88(4):247–264.zbMATHCrossRefGoogle Scholar
  66. Moxon, K. A., Gerhardt, G. A., and Adler, L. E., 2003b, Dopaminergic modulation of the P50 auditory evoked potential in a computer model of the CA3 region of the hippocampus: Its relationship to sensory gating in schizophrenia, Biol. Cyber. 88(4):265–275.zbMATHCrossRefGoogle Scholar
  67. Moxon, K. A., Leiser, S. C., Gerhardt, G. A., Barbee, K., and Chapin, J. K., 2004, Ceramic based multisite electrode arrays for electrode recording, IEEE Trans. Biomed. Eng. 51(4):647–656.CrossRefGoogle Scholar
  68. Mountcastle, V. B., 1957, Modularity and topographic properties of single neurons of cat’s somatic sensory cortex, J. Neurophysiol. 20:408–434.Google Scholar
  69. Mountcastle, V. B., Lynch, J. C., Georgopoulus, A., Sakata, H., and Acuna, C., 1975, Posterior parietal association cortex of the monkey: Command functions for operations within extrapersonal space, J. Neurophysiol. 38(4):871.Google Scholar
  70. Najafi, K., and Wise, K., 1986, An implantable multielectrode array with on-chip signal processing, IEEE J. Solid-State Circuits 21:1035–1044.CrossRefGoogle Scholar
  71. Nguyen, D. P., Frank, L. M., and Brown, E. N., 2003, An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings, Network 14(1):61–82.CrossRefGoogle Scholar
  72. Nicolelis, M. A., 2003, Brain-machine interfaces to restore motor function and probe neural circuits, Nat. Rev. Neurosci. 4(5):417–422.CrossRefGoogle Scholar
  73. Nicolelis, M. A., and Fanselow, E. E., 2002, Thalamocortical optimization of tactile processing according to behavioral state, Nat. Neurosci. 5(6):517–523.CrossRefGoogle Scholar
  74. Nicolelis, M. A. L., Lin, R. C. S., Woodward, D. J., and Chapin, J. K., 1993, Dynamic and distributed properties of many-neuron ensembles in the ventral posterior medial thalamus of awake rats, Proc. Natl. Acad. Sci. U.S.A. 90:2212.CrossRefGoogle Scholar
  75. Nicolelis, M. A. L., Baccala, L. A., Lin, R. C. S., and Chapin, J. K., 1995, Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system, Science 268:1353.CrossRefGoogle Scholar
  76. Nordhausen, C. T., Maynard, E. M., and Normann, R. A., 1996, Single unit recording capabilities of a 100 microelectrode array, Brain Res. 726:129.CrossRefGoogle Scholar
  77. Obeid, I. M., Morizio, J. C., Moxon, K. A., Nicolelis, M. A. L., and Wolf, P. D., 2003, Two multichannel integrated circuits for neural recording and signal processing, IEEE Trans. Biomed. Eng. 50(2):255–258.CrossRefGoogle Scholar
  78. Obeid, I., Nicolelis, M. A., and Wolf, P. D., 2004, A multichannel telemetry system for single unit neural recordings, J. Neurosci. Methods. 133(1–2):33–38.CrossRefGoogle Scholar
  79. Peckham, P. H., Kilgore, K. L., and Keith, M. W., 2000, Advances in upper extremity functional restoration employing neuroprostheses, In: Neural Prostheses for Restoration of Sensory and Motor Function (J. K. Chapin and K. A. Moxon, eds.), CRC Press, Boca Raton, pp. 45–74.Google Scholar
  80. Pochay, P., Wise, K. D., Allard, L. F., and Rutledge, L. T., 1979, A multichannel depth array fabricated using electron-beam lithography, IEEE Trans. Biomed. Eng. 26(4):199–206.CrossRefGoogle Scholar
  81. Pouzat, C., Mazor, O., and Laurent, G., 2002, Using noise signature to optimize spike-sorting and to assess neuronal classification quality, J. Neurosci. Methods 122(1):43–57.CrossRefGoogle Scholar
  82. Principe, J. C., Euliano, N. R., and Lefebvre, W. C., 2000, Neural and Adaptive Systems, John Wiley & Sons, New York.Google Scholar
  83. Prohaska, O. J., Olcaytug, F., Pfundner, P., and Draguan, H., 1986, “Thin-film multiple electrode probes: Possibilities and limitations,” IEEE Trans. Biomed. Eng. 33(2):223–229.CrossRefGoogle Scholar
  84. Rolls, E. T., Treves, A., Robertson, R. G., Georges-Francois, P., and Panzeri, S., 1998, Information about spatial view in an ensemble of primate hippocampal cells, J. Neurophysiol. 79:1797–1813.Google Scholar
  85. Rolls, E. T., Treves, A., and Tovee, M. J., 1997, The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex, Exp. Brain Res. 114:149–162.CrossRefGoogle Scholar
  86. Rosenblith, W. A., 1957, Relations between auditory psychophysics and auditory electrophysiology, Trans. N. Y. Acad. Sci. 19(7):650–657.Google Scholar
  87. Rousche, R. J., and Norman, R. A., 1998, Chronic recording capability of the Utah intracortical electrode array in cat sensory cortex, J. Neurosci. Methods 82:1–15.CrossRefGoogle Scholar
  88. Rousche, P. J., Petersen, R. S., Battiston, S., Giannotta, S., and Diamond, M. E., 1999, Examination of the spatial and temporal distribution of sensory cortical activity using a 100-electrode array, J. Neurosci. Methods 90:57.CrossRefGoogle Scholar
  89. Rubenstein, J. T., and Miller, C. A., 1999, How do cochlear prostheses work? Curr. Opin. Neurobiol. 4:399–404.CrossRefGoogle Scholar
  90. Schmidt, E. M., 1980, Single neuron recording from motor cortex as a possible source of signals for control of external devices, Ann. Biomed. Eng. 8(4–6):339–349.CrossRefGoogle Scholar
  91. Schmidt, E. M., 1999, Electrodes for many single neuron recordings, in Methods for Neural Ensemble Recordings (M. A. L. Nicolelis, ed.), CRC Press, New York.Google Scholar
  92. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., and Donoghue, J. P., 2002, Instant neural control of a movement signal, Nature 416(6877):141–142.CrossRefGoogle Scholar
  93. Sherrington, C. S., 1906, The Integrative Activity of the Nervous System, Yale University Press, New Haven.Google Scholar
  94. Smith, B., Tang, Z., Johnson, M. W., Pourmehdi, S., Gazdik, M. M., Buckett, J. R., and Peckham, P. H., 1987, An externally powered, multichannel, implantable stimulator-telemeter for control of paralyzed muscle, IEEE Trans. Biomed. Eng. 45(4):463–475.CrossRefGoogle Scholar
  95. Sutter, E. E., 1992, The brain response interface: Communication through visually-induced electrical brain responses, J. Microcomp. Appl. 15:31–45.CrossRefGoogle Scholar
  96. Szabo, I., and Marczynski, T. J., 1993, A low-noise preamplifier for multisite recording of brain multi-unit activity in freely moving animals, J. Neurosci. Methods 47:33–38.CrossRefGoogle Scholar
  97. Szentagothai, J., 1975, The “module-concept” in cerebral cortex architecture, Brain Res. 95:475–496.CrossRefGoogle Scholar
  98. Takahashi, K., and Matsuo, T., 1984, Integration of multi-microelectrode and interface circuits by silicon planar and three-dimensional fabrication technology, Sens. Actuat. 5(1):89–99.CrossRefGoogle Scholar
  99. Takeuchi, S., and Shimoyama, I., 2004, A radio-telemetry system with a shape memory alloy microelectrode for neural recording of freely moving insects, IEEE Trans. Biomed. Eng. 51(1):133–137.CrossRefGoogle Scholar
  100. Taylor, D. M., Tillery, S. I. H., and Schwartz, A. B., 2002, Direct cortical control of 3D neuroprosthetic device, Science 296:1829–1832.CrossRefGoogle Scholar
  101. Towe, B., 1986, Passive biotelemetry by frequency keying, IEEE Trans. Biomed. Eng. 33.Google Scholar
  102. Vallabhaneni, A., Wang, T., and He, B., 2005, Brain-Computer Interface, In He (Eds): Neural Engineering, Kluwer Academic Publishers.Google Scholar
  103. Vetter, R. J., Williams, J. C., Hetke, J. F., Nunamaker, E. A., Kipke, D. R., 2004, Chronic neural recording using silicon-substrate microelectrode arrays implanted in cerebral cortex, IEEE Trans. Biomed. Eng. 51(6):896–904.CrossRefGoogle Scholar
  104. Vittoz, E., Borel, J., Gentil, P., Noblanc, J., Nouailhat, A., and Verdone, M., 1993, Design of low-voltage low-power IC’s, In: Proceedings of the 23rd European Solid State Device Research Conference, p. 927.Google Scholar
  105. Wessberg, J., Stambaugh, C. R., Kralik, J. D., Beck, P. D., Laubach, M., Chapin, J. K., Kim, J., Biggs, S. J., Srinivasan, M. A., and Micolelis, M. A. L., 2000, Real-time prediction of hand trajectory by ensembles of cortical neurons in primates, Nature 48:361–365.CrossRefGoogle Scholar
  106. Wheeler, B. C., 1999, Automatic discrimination of singe units, In: Methods for Neural Ensemble Recordings (M. A. L. Nicolelis, ed.), CRC Press, New York, p. 61.Google Scholar
  107. White, R. L., and Gross, T. J., 1974, An evaluation of the resistance to electrolysis of metals for use in biostimulation microprobes, IEEE Trans. Biomed. Eng. 21:487.CrossRefGoogle Scholar
  108. White, R. L., Roberts, L. A., Cotter, N. E., Kwon, O. H., 1983, Thin-film electrode fabrication techniques, Ann. N.Y. Acad. Sci. 83:183–190.CrossRefGoogle Scholar
  109. Williams, J. C., Rennaker, R. L., and Kipke, D. R., 1999, Long-term recording characteristics of wire microelectrode arrays implanted in cerebral cortex, Brain Res. Prot. 4:303–313.CrossRefGoogle Scholar
  110. Wilson, M. A., and McNaughton, B. L., 1996a, Dynamics of the hippocampal ensemble code for space, Science 261:1055.CrossRefGoogle Scholar
  111. Wilson, M. A., and McNaughton, B. L., 1996b, Reactivation of hippocampal ensemble memories during sleep, Science 265:6761.Google Scholar
  112. Wise, K., 1998, Micromachined interfaces to the cellular world, Sens. Mater. 10:385–395.Google Scholar
  113. Wise, K., and Angell, J., 1975, A low-capacitance multielectrode probe for use in extracellular neurophysiology, IEEE Trans. Biomed. Eng. 22:212–219.CrossRefGoogle Scholar
  114. Wise, K. D., Angell, J. B., Starr, A., 1970, Integrated circuit approach to extracellular microelectrodes, IEEE Trans. Biomed. Eng. 17(3):238–246.Google Scholar
  115. Wise, K. D., and Najafi, K., 1991, Microfabrication techniques for integrated sensors and microsystems, Science 254:1335–1342.CrossRefGoogle Scholar
  116. Wise, K. D., Najafi, K., and Drake, K. L., 1994, A multichannel microprobe for intracortical single-unit recordings, Proc. IEEE/NSF Symp. Biosens., 87–89.Google Scholar
  117. Wise, K. D., and Weissman, R. H., 1971, Thin films of glass and their application to biomedical sensors, Med. Biol. Eng. 9:339–350.CrossRefGoogle Scholar
  118. Wolpaw, J. R., McFarland, D. J., and Vaughan, T. M., 2000, Brain-computer interface research at the Wadsworth Center, IEEE Trans. Rehabil. Eng. 8:222–225.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic/Plenum Publishers 2005

Authors and Affiliations

  • Karen A. Moxon
    • 1
    • 2
  1. 1.School of Biomedical EngineeringDrexel UniversityPhiladelphia
  2. 2.Department of Neurobiology and Anatomy, College of MedicineDrexel UniversityPhiladelphia

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