Columnar EEG magnetic influences on molecular development of short-term memory

Short-Term Memory New Research

Volumn , Issue , 2012, Pages 38-72

  Ingber, Lester ^a  

^a Wacker Dr PO Sears Tower   (United States)

Author keywords

Astrocytes; Neocorticaldynamics; Short termmemory; Vectorpotential

Indexed keywords

EID: 84892095573     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (112)

1. Alexander, J., Fuss, B., Colello, R, (2006). Electric field-induced astrocyte alignment directs neurite outgrowth. Neuron Glia Biology, 2(2), 93-103.
2. Amzica, F., Massimini, M, (2002). Glial and neuronal interactions during slow wave and paroxysmal activities in the neocortex. Cerbral Cortex, 12(10), 1101-1113.
3. Anastassiou, C., Perin, R., Markram, H., Koch, C, (2011). Ephaptic coupling of cortical neurons. NatureNeuroscience, 14, 217-223.
4. Balduzzi, D., Tononi, G, (2009). Qualia: The geometry of integrated information. PLoS Computational Biology, 5(8), 1-24.
5. Banaclocha, M, (2005). Magnetic storage of information in the human cerebral cortex: A hypothesis for memory. International Journal of Neuroscience, 115(3), 329-337.
6. Banaclocha, M, (2007). Neuromagnetic dialogue between neuronal minicolumns and as-troglial network: A new approach for memory and cerebral computation. Brain Research Bulletin, 73, 21-27.
7. Banaclocha, M. (2011). Magnetic bubbles in brain neocortex: a new model for neuro-cognitive functions. Technical Report Report, Hospital Lluis Alcanyis, Jativa, Spain.
8. Banaclocha, M., Banaclocha, H, (2010). Spontaneous neocortical activity and cognitive functions: A neuron-astroglial bio-magnetic and self-organized process. NeuroQuantol-ogy, 8(2), 191-199.
9. Banaclocha, M., Bookkon, I., Banaclocha, H, (2010). Long-term memory in brain magnetite. Medical Hypotheses, 74(2), 254-257.
10. Bellinger, S, (2005). Modeling calcium wave oscillations in astrocytes. Neurocomputing, 65(66), 843-850.
11. Boyd, I., Martin, A. (1956). The end-plate potential in mammalian muscle. Journal of Physiology (London) 132,74-91.
12. Buxhoeveden, D., Casanova, M, (2002). The minicolumn hypothesis in neuroscience. Brain, 125(5), 935-951. URL http://tinyurl.com/bc2002brain.
13. Caille', A., Pink, D., de Verteuil, F., Zuckermann, M, (1980). Theoretical models for quasi-two-dimensional mesomorphic monolayers and membrane bilayers. Canadian Journal of Physics, 58, 1723-1726.
14. Colombo, J., Reisin, H., Jones, M., Bentham, C, (2005). Development of interlaminar astroglial processes in the cerebral cortex of control and down's syndrome human cases. Experimental Neurology, 193, 207-217.
15. Cowan, J, (1982). Spontaneous symmetry breaking in large scale nervous activity. International Journal ofQuantum Chemistry, 22, 1059-1082.
16. Cragg, B., Temperley, H. (1954). The organization of neurons: A cooperative analogy. ElectroencephalographyClinical Neurophysiology 6, 85-92.
17. Ericsson, K., Chase, W, (1982). Exceptional memory. American Scientist, 70, 607-615.
18. Feynman, R., Leighton, R., Sands, M. (1964). Chapter 15: The vector potential. In The Feynman Lectures on Physics, Addison-Wesley, Reading, MA, volume II. 1-16.
19. Georgiev, D. (2003). Electric and magnetic ields inside neurons and their impact upon the cytoskeletal microtubules. Technical Report Cogprints Report, Cogprints, U. Southampton, UK. http://cogprints.org/3190/.
20. Giuliani, G, (2010). Vector potential, electromagnetic induction and 'physical meaning'. European Journal of Physics, 31(4), 871-880.
21. Goldberg, M., De Pitta, M., Volman, V., Berry, H., Ben-Jacob, E, (2010). Nonlinear gap junctions enable long-distance propagation of pulsating calcium waves in astrocyte networks. PLoS Computational Biology, 6(8), 1-14.
22. Gordon, G., Iremonger, K., Kantevari, S., Ellis-Davies, G., MacVicar, B., Bains, J, (2009). Astrocyte-mediated distributed plasticity at hypothalamic glutamate synapses. Neuron, 64, 391-403.
23. Graham, R, (1977). Covariant formulation of non-equilibrium statistical thermodynamics. Zeitschrift fur Physik B, 26, 397-405.
24. Graham, R. (1978). Path-integral methods in nonequilibrium thermodynamics and statistics. In L. Garrido, P. Seglar, P. Shepherd (Eds.), Stochastic Processes in Nonequilibrium Systems, Springer, New York, NY. 82-138.
25. Hagan, S., Hameroff, R., Tuszyriski, J, (2002). Quantum computation in brain microtubules: Decoherence and biological feasibility. Physical Review E, 65(061901), 1-11.
26. Hick, W, (1952). On the rate of gains of information. Quarterly Journal Experimental Psychology, 34(4), 1-33.
27. Ingber, L, (1981). Towards a uniied brain theory. Journal Social Biological Structures, 4, 211-224. URL http://www.ingber.com/smni81_unified.pdf.
28. Ingber, L, (1982). Statistical mechanics of neocortical interactions. i. basic formulation. Physica D, 5, 83-107. URL http://www.ingber.com/smni82_basic.pdf.
29. Ingber, L, (1983). Statistical mechanics of neocortical interactions. dynamics of synaptic modiication. Physical Review A, 28, 395-416. URL http://www.ingber.com/smni83_dynamics.pdf.
30. Ingber, L, (1984). Statistical mechanics of neocortical interactions. derivation of short-term-memory capacity. Physical Review A, 29, 3346-3358. URL http://www.ingber.com/smni84_stm.pdf.
31. Ingber, L, (1985a). Statistical mechanics of neocortical interactions. EEG dispersion relations. IEEE Transactions Biomedical Engineering, 32, 91-94. URL http://www.ingber.com/smni85_eeg.pdf.
32. Ingber, L, (1985b). Statistical mechanics of neocortical interactions: Stability and duration ofthe 7+-2rule of short-term-memory capacity. Physical Review A, 31, 1183-1186. URL http://www.ingber.com/smni85_stm.pdf.
33. Ingber, L. (1985c). Statistical mechanics algorithm for response to targets (SMART). In Workshop on Uncertainty and Probability in Artificial Intelligence: UC Los Angeles, 14-16 August 1985, American Association for Artificial Intelligence, Menlo Park, CA. 258-264. URL http://www.ingber.com/combat85_smart.pdf.
34. Ingber, L. (1986). Statistical mechanics of neocortical interactions. Bulletin American Physical Society 31, 868.
35. Ingber, L, (1991). Statistical mechanics of neocortical interactions: A scaling paradigm applied to electroencephalography. Physical Review A, 44(6), 4017-4060. URL http://www.ingber.com/smni91_eeg.pdf.
36. Ingber, L. (1992). Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions. Physical Review A 45(4), R2183-R2186. URL http://www.ingber.com/smni92_mnn.pdf.
37. Ingber, L. (1993). Adaptive simulated annealing (ASA). Technical Report Global optimization C-code, Caltech Alumni Association, Pasadena, CA. URL http://www.ingber.com/#ASA-CODE.
38. Ingber, L. (1994). Statistical mechanics of neocortical interactions: Path-integral evolution of short-term memory. Physical Review E 49(5B), 4652-4664. URL http://www.ingber.com/smni94_stm.pdf.
39. Ingber, L. (1995a). Statistical mechanics of multiple scales of neocortical interactions. In P. Nunez (Ed.), Neocortical Dynamics and Human EEG Rhythms, Oxford University Press, New York, NY. 628-681. ISBN 0-19-505728-7. URL http://www.ingber.com/smni95_scales.pdf.
40. Ingber, L, (1995b). Statistical mechanics of neocortical interactions: Constraints on 40 hz models of short-term memory. Physical Review E, 52(4), 4561-4563. URL http://www.ingber.com/smni95_stm40hz.pdf.
41. Ingber, L. (1996a). Statistical mechanics of neocortical interactions: Multiple scales of EEG. In R. Dasheiff, D. Vincent (Eds.), Frontier Science in EEG: Continuous Waveform Analysis (Electroencephalography Clinical Neurophysiology Suppl. 45), Elsevier, Amsterdam. 79-112. Invited talk to Frontier Science in EEG Symposium, New Orleans, 9 Oct 1993. ISBN 0-444-82429-4. URL http://www.ingber.com/smni96_eeg.pdf.
42. Ingber, L. (1996b). Nonlinear nonequilibrium nonquantum nonchaotic statistical mechanics of neocortical interactions. Behavioral and Brain Sciences 19(2), 300-301. Invited commentary on Dynamics of the brain at global and microscopic scales: Neural networks and the EEG, by J.J. Wright and D.T.J. Liley. URL http://www.ingber.com/smni96_nonlinear.pdf.
43. Ingber, L. (1997). Statistical mechanics ofneocortical interactions: Applications ofcanoni-cal momenta indicators to electroencephalography. Physical Review E55(4), 4578-4593. URL http://www.ingber.com/smni97_cmi.pdf.
44. Ingber, L. (1998). Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG. Mathematical Computer Modelling 27(3), 3364. URL http://www.ingber.com/smni98_cmi_test.pdf.
45. Ingber, L. (1999). Statistical mechanics of neocortical interactions: Reaction time correlates of the g factor. Psycholoquy 10(068). Invited commentary on The g Factor: The Science of Mental Ability by Arthur Jensen. URL http://www.ingber.com/smni99_g_factor.pdf.
46. Ingber, L. (2000). High-resolution path-integral development ofinancial options. Physica A 283(3-4), 529-558. URL http://www.ingber.com/markets00_highres.pdf.
47. Ingber, L. (2005). Trading in risk dimensions (TRD). Technical Report Report 2005:TRD, Lester Ingber Research, Ashland, OR. URL http://www.ingber.com/markets05_trd.pdf.
48. Ingber, L. (2006). Ideas by statisticalmechanics (ISM). Technical ReportReport2006:ISM, Lester Ingber Research, Ashland, OR. URL http://www.ingber.com/smni06_ism.pdf.
49. Ingber, L. (2007). Ideas by statistical mechanics (ISM). Journal Integrated Systems Design and Process Science 11(3), 31-54. Special Issue: Biologically Inspired Computing.
50. Ingber, L. (2008). AI and ideas by statistical mechanics (ISM). In J. Rabunal, J. Dorado, A. Pazos (Eds.), Encyclopedia of Artificial Intelligence, Information Science Reference, New York. 58-64. ISBN 978-1-59904-849-9.
51. Ingber, L. (2009a). Statistical mechanics of neocortical interactions: Columnar EEG. Technical Report Report 2009:CEEG, Lester Ingber Research, Ashland, OR. URL http://www.ingber.com/smni09_columnar_eeg.pdf.
52. Ingber, L, (2009b). Statistical mechanics of neocortical interactions: Nonlinear columnar electroencephalography. NeuroQuantology Journal, 7(4), 500-529. URL http://www.ingber.com/smni09_nonlin_column_eeg.pdf.
53. Ingber, L. (2010). Trading in risk dimensions. In G. Gregoriou (Ed.), The Handbook of Trading: Strategies for Navigating and Profiting from Currency, Bond, and Stock Markets, McGraw-Hill, New York. 287-300.
54. Ingber, L. (2012a). Asa options. In H. Oliveira, A. Petraglia, L. Ingber, M. Machado, M. Petraglia (Eds.), Stochastic global optimization and its applications with fuzzy adaptive simulated annealing, Springer, New York. (to be published). Invited Paper.
55. Ingber, L. (2012b). Computational algorithms derived from multiple scales of neocortical processing. In A. Pereira, Jr, E. Massad, N. Bobbitt (Eds.), Pointing at Boundaries: Integrating Computation and Cognition on Biological Grounds, Springer, New York. (to be published). Invited Paper.
56. Ingber, L., Chen, C., Mondescu, R., Muzzall, D., Renedo, M, (2001). Probability tree algorithm for general diffusion processes. Physical Review E, 64(5), 056702-056707. URL http://www.ingber.com/path01_pathtree.pdf.
57. Ingber, L., Fujio, H., Wehner, M, (1991). Mathematical comparison of combat computer models to exercise data. Mathematical Computer Modelling, 15(1), 65-90. URL http://www.ingber.com/combat91_data.pdf.
58. Ingber, L., Nunez, P, (1990). Multiple scales of statistical physics of neocortex: Application to electroencephalography. Mathematical Computer Modelling, 13(7), 83-95.
59. Ingber, L., Nunez, P, (1995). Statistical mechanics of neocortical interactions: High resolution path-integral calculation of short-term memory. Physical Review E, 51(5), 5074-5083. URL http://www.ingber.com/smni95_stm.pdf.
60. Ingber, L., Nunez, P. (2010). Neocortical dynamics at multiple scales: Eeg standing waves, statistical mechanics, and physical analogs. Mathematical Biosciences 229, 160-173. doi:10.1016/j.mbs.2010.12.003 URL http://www.ingber.com/smni10_multiple_scales.pdf.
61. Irimia, A., Swinney, K., Wikswo, J, (2009). Partial independence of bioelectric and biomag-netic ield and its implications for encephalography and cardiography. Physical Review E, 79(051908), 1-13.
62. Jackson, J. (1962). Classical Electrodynamics. Wiley & Sons, New York.
63. Jensen, A. (1987). Individual differences in the hick paradigm. In P. Vernon (Ed.), Speed of Information-Processing and Intelligence, Ablex, Norwood, NJ. 101-175.
64. Jirsa, V., Haken, H, (1996). Field theory of electromagnetic brain activity. Physical Review Letters, 77(5), 960-963.
65. Johnsen, S., Lohmann, K, (2008). Magnetoreception in animals. Physics Today, 61, 29-35.
66. Katz, B. (1966). Nerve, Muscle, and Synapse. McGraw-Hill, new York, NY.
67. Kominis, I. (2009). Zeno is pro darwin: quantum zeno effect suppresses the dependence of radical-ion-pair reaction yields on exchange and dipolar interactions. Technical Report arXiv:0908.0763v2 [quant-ph], University of Crete, Greece.
68. Korn, H., Mallet, A, (1984). Transformation of binomial input by the postsynaptic membrane at a central synapse. Science, 225, 1157-1159.
69. Korn, H., Mallet, A., Faber, D, (1981). Fluctuating responses at a central synapse: n of binomial it predicts number of stained presynaptic boutons. Science, 213, 898-900.
70. Langouche, F., Roekaerts, D., Tirapegui, E, (1979). Discretization problems of functional integrals in phase space. Physical Review D, 20, 419-432.
71. Langouche, F., Roekaerts, D., Tirapegui, E. (1982). Functional Integration and Semiclas-sical Expansions. Reidel, Dordrecht, The Netherlands.
72. Little, W, (1974). The existence of persistent states in the brain. Mathematical Biosciences, 19, 101-120.
73. Little, W., Shaw, G, (1978). Analytic study of the memory storage capacity of a neural network. Mathematical Biosciences, 39, 281-290.
74. Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., Wu, C. (2004). Interneurons of the neocortical inhibitory system. Nature Reviews 5, 793-807.
75. Mathews, J., Walker, R. (1970). Mathematical Methods of Physics, 2nd ed. Benjamin, New York, NY.
76. McFadden, J. (2007). Conscious electromagnetic ield theory. NeuroQuantology 5(3), 262-270.
77. Miller, G, (1956). The magical number seven, plus or minus two. PsychologyReview, 63, 81-97.
78. Moore, C., Cao, R, (2008). The hemo-neural hypothesis: On the role of blood flow in information processing. Journal ofNeurophysiology, 99, 2035-2047.
79. Mountcastle, V. (1978). An organizing principle for cerebral function: The unit module and the distributed system. In G. Edelman, V. Mountcastle (Eds.), The Mindful Brain, Massachusetts Institute ofTechnology, Cambridge. 7-50.
80. Mountcastle, V., Andersen, R., Motter, B, (1981). The influence of attentive fixation upon the excitability of the light-sensitive neurons of the posterior parietal cortex. Journal of Neuroscience, 1, 1218-1235.
81. Murakami, S., Okada, Y, (2006). Contributions of principal neocortical neurons to magne-toencephalography and electroencephalography signals. Journal ofPhysiology, 575(3), 925-936.
82. Murdock, Jr, B, (1983). A distributed memory model for serial-order information. Psychology Review, 90, 316-338.
83. Nakano, T., Suda, T., Koujin, T., Haraguchi, T., Hiraoka, Y. (2007). Molecular communication through gap junction channels: System design, experiments and modeling. In Proceedings 2nd International Conference on Bio-Insprired Models ofNetwork, Information, andComputingSystems, Institute forComputerSciences, Social-Informatics and Telecommunications Engineering, Budapest, Hungary. 139-146.
84. Nunez, P. (1981). Electric Fields ofthe Brain: The Neurophysics ofEEG. Oxford University Press, London.
85. Nunez, P, (1989). Generation of human EEG rhythms by a combination of long and short-range neocortical interactions. Brain Topography, 1, 199-215.
86. Nunez, P., Srinivasan, R. (2006). Electric Fields ofthe Brain: The Neurophysics ofEEG, 2nd Ed. Oxford University Press, London.
87. Oberheim, N., Takano, T., Han, X., He, W., Lin, J., Wang, F., Xu, Q., Wyatt, J., Pilcher, W., Ojemann, J., Ransom, B., Goldman, S., Nedergaard, M, (2009). Uniquely hominid features of adult human astrocytes. Journal ofNeuroscience, 29(10), 3276-3287.
88. Pereira, Jr, A., Furlan, F, (2009). On the role of synchrony for neuron-astrocyte interactions and perceptual conscious processing. Journal ofBiological Physics, 35(4), 465-480.
89. Pereira, Jr, A., Furlan, F, (2010). Astrocytes and human cognition: Modeling information integration and modulation of neuronal activity. Progress in Neurobiology, 92, 405-420.
90. Perkel, D., Feldman, M, (1979). Neurotransmitter release statistics: Moment estimates for inhomogeneous bernoulli trials. Journal ofMathematical Biology, 7, 31-40.
91. Postnov, D., Koreshkov, R., Brazhe, N., Brazhe, A., Sosnovtseva, O, (2009). Dynamical patterns of calcium signaling in a functional model of neuron-astrocyte networks. Journal ofBiological Physics, 35, 425-445.
92. Rabinovich, M., Varona, P., Selverston, A., Arbaranel, H, (2006). Dynamical principles in neuroscience. Reviews Modern Physics, 78(4), 1213-1265.
93. Rakic, P, (2008). Confusing cortical columns. Proceedings ofthe National Academy of Sciences, 105(34), 12099-12100. URL http://www.pnas.org/content/105/34/12099.full.
94. Reisin, H., Colombo, J. (2002). Considerations on the astroglial architecture and the columnar organization of the cerebral cortex. Cellular and Molecular Neurobiology 22(5/6), 633-644.
95. Risken, H. (1989). The Fokker-Planck Equation: Methods ofSolution and Applications. Springer-Verlag, Berlin.
96. Rodgers, C., Hore, P, (2009). Chemical magnetoreception in birds: The radical pair mechanism. Proceedings ofthe National Academy ofSciences, 106(2), 353-360.
97. Roth, B., Wikswo, Jr, J, (1985). The magnetic field of a single axon. Biophysical Journal, 48, 93-109.
98. Scemes, E., Suadicani, S., Spray, D. (2000). Intercellular calcium wave communication via gap junction dependent and independent mechanisms. In Current Topics in Membranes, Academic Press, New York, NY, volume 49. 145-173.
99. Schipke, C., Boucsein, C., Ohlemeyer, C., Kirchhoff, F., Kettenmann, H, (2002). Astrocyte ca2+ waves trigger responses in microglial cells in brain slices. Federation ofAmerican Societies for Experimental Biology Journal, 16, 255-257.
100. Schulman, L. (1981). Techniques and Applications ofPath Integration. J. Wiley & Sons, New York.
101. Scott, A, (1975). The electrophysics ofa nerve iber. Reviews Modern Physics, 47, 487-533.
102. Shaw, G., Vasudevan, R, (1974). Persistent states of neural networks and the random nature of synaptic transmission. Mathematical Biosciences, 21, 207-218.
103. Shepherd, G. (1979). The Synaptic Organization of the Brain. 2nd ed. Oxford University Press, New York, NY.
104. Silberstein, R. (1995). Neuromodulation of neocortical dynamics. In P. Nunez (Ed.), Neo-cortical Dynamics and Human EEG Rhythms, Oxford University Press, New York, NY. 628-681.
105. Solov'yov, I., Schulten, K, (2009). Magnetoreception through cryptochrome may involve superoxide. Biophysical Journal, 96(12), 4804-4813.
106. Sommerhoff, G. (1974). Logic ofthe Living Brain. Wiley, New York, NY.
107. Tollaksen, J., Aharonov, Y., Casher, A., Kaufherr, T., Nussinov, S, (2010). Quantum interference experiments, modular variables and weak measurements. New JournalofPhysics, 12(013023), 1-29.
108. Tsal, Y, (1983). Movements of attention across the visual field. Journal of Experimental Psychology, 9, 523-530.
109. von der Heydt, I., von der Heydt, N., Obermair, G, (1981). Statistical model of current-coupled ion channels in nerve membranes. Zeitschrift fur Physik, 41, 153-164.
110. Vu, E., Krasne, F, (1992). Evidence for a computational distinction between proximal and distal neuronal inhibition. Science, 255, 1710-1712.
111. Zhang, G., Simon, H, (1985). STM capacity for chinese words and idioms: Chunking and acoustical loop hypotheses. Memory & Cognition, 13, 193-201.
112. Zhang, X., Begleiter, H., Porjesz, B., Wang, W., Litke, A, (1995). Event related potentials during object recognition tasks. Brain Research Bulletin, 38(6), 531-538.