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A columnar model explaining long-term memory

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Abstract

A hologram provides a useful model for explaining the associative memory of the brain. Recent advances in neuroscience emphasize that single neurons can store complex information and that subtle changes in neurons underlie the process of memorization. Experimental results suggest that memory has a localized character. This finding is inconsistent with the characteristics of holographic memory, because this memory has a delocalized, uniform distribution of refractive index in the recorded medium. The recently proposed columnar memory model has a discrete distribution of refractive index. In this study, we first examined the performance of columnar memory and found that it was comparable to holographic memory. Secondly, we showed that this model could explain the above-mentioned experimental results as well as associative memory.

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References

  1. Kameyama, M., Lithography and ITRS, Technical Report 10-06, IIR Working Paper, Institute of Innovation Research Hitotsubashi University, 2010.

  2. Bloom, F.E., Lazerson, A., and Hofstadter, L., Brain, Mind, and Behavior., New York: W.H. Freeman, 1985.

    Google Scholar 

  3. Azevedo, F.A., Carvalho, L.R., Grinberg, L.T., Farfel, J.M., Ferretti, R.E., Leite, R.E., Filho, W.J., Lent, R., and Herculano-Houzel, S., Equal Numbers of Neuronal and Nonneuronal Cells Make the Human Brain an Isometrically Scaled-Up Primate Brain, J. Comp. Neurol., 2009, vol. 513, pp. 532–541.

    Article  Google Scholar 

  4. Shepherd, G.M., The Synaptic Organization of the Brain, 5th ed., New York: Oxford University Press, 2003.

    Google Scholar 

  5. Nunez, P.L. and Srinivasan, R., Electric Fields of the Brain: The Neurophysics of EEG, New York: Oxford University Press, 2005.

    Google Scholar 

  6. Moravec, H., When Will Computer Hardware Match the Human Brain?, J. Evolution Technol., 1998, vol. 1, pp. 1–10.

    Google Scholar 

  7. Hopfield, J.J., Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proc. Natl Acad. Sci., 1982, vol. 79, pp. 2554–2558.

    Article  MathSciNet  Google Scholar 

  8. Abbott, L.F. and Kepler, T.B., Model Neurons: From Hodgkin-Huxley to Hopfield, Lect. Notes Phys., 1990, vol. 368, pp. 5–18.

    Article  Google Scholar 

  9. Pasemann, F., A Simple Chaotic Neuron, Physica, Ser. D, 1997, vol. 104, pp. 205–211.

    Article  MATH  Google Scholar 

  10. Heil, P., Neubauer, H., Irvine, D.R.F., and Brown, M., Spontaneous Activity of Auditorynerve Fibers: Insights into Stochastic Processes at Ribbon Synapses, J. Neurosci., 1994, vol. 27, pp. 8457–8474.

    Article  Google Scholar 

  11. Rolls, E.T. and Treves, A., Neural Networks and Brain Function, New York, Oxford: Oxford University Press, 1998.

    Google Scholar 

  12. Richter, J.D. and Klann, E., Making Synaptic Plasticity and Memory Last: Mechanisms of Translational Regulation, Genes Dev., 2009, vol. 23, pp. 1–11.

    Article  Google Scholar 

  13. Mizutani, R., Takeuchi, A., Uesugi, K., Takekoshi, S., Osamura, R.Y., and Suzuki, Y., Microtomographic Analysis of Neuronal Circuits of Human Brain, Cereb. Cortex, 2010, vol. 20, pp. 1739–1748.

    Article  Google Scholar 

  14. Sasaki, K.S., Tabuchi, Y., and Ohzawa, I., Complex Cells in the Cat Striate Cortex Have Multiple Disparity Detectors in the Three-Dimensional Binocular Receptive Fields, J. Neurosci., 2010, vol. 30, pp. 13826–13837.

    Article  Google Scholar 

  15. Quiroga, R., Reddy, L., Kreiman, G., and Koch, C., Invariant Visual Representation by Single Neurons in the Human Brain, Nature, 2005, vol. 435, pp. 1102–1107.

    Article  Google Scholar 

  16. Kubota, Y., Karube, F., Nomura, M., Gulledge, A.T., Mochizuki, A., Schertel, A., and Kawaguchi, Y., Conserved Properties of Dendritic Trees in Four Cortical Interneuron Subtypes, Sci. Rep., 2011, vol. 1, pp. 1–13.

    Article  Google Scholar 

  17. Hoshino, T., Yatagai, T., and Itoh, M., Precise and Rapid Distance Measurements by Scatterometry, Opt. Express., 2012, vol. 20, pp. 3954–3966.

    Article  Google Scholar 

  18. Fields, R.D., Making Memories Stick, Sci. Am., 2005, vol. 292, pp. 74–81.

    Article  Google Scholar 

  19. Fusi, S. and Abbott, L.F., Limits on the Memory Storage Capacity of Bounded Synapses, Nat. Neurosci., 2007, vol. 10, pp. 485–493.

    Google Scholar 

  20. Thivierge, J.-P., Dandurand, F., and Cousineau, D., A Multi-State Model of Cortical Memory, in Proceedings of International Joint Conference on Neural Networks, IEEE, USA, California, San Jose, 2011, pp. 133–138.

    Google Scholar 

  21. Gazzaniga, M.S., The Cognitive Neurosciences, 2nd ed., Oxford, New York: W.W. Norton & Company, 2002.

    Google Scholar 

  22. Kuroyanagi, S. and Iwata, A., Auditory Pulse Neural Network Model to Extract the Interaural Time and Level Difference for Sound Localization, IEICE T. Inf. Syst., 1994, vol. E77-D, pp. 466–474.

    Google Scholar 

  23. Heil, P., Neubauer, H., Irvine, D.R.F., and Brown, M., Spontaneous Activity of Auditorynerve Fibers: Insights into Stochastic Processes at Ribbon Synapses, J. Neurosci., 1994, vol. 27, pp. 8457–8474.

    Article  Google Scholar 

  24. Westlake, P.R., The Possibilities of Neural Holographic Processes within the Brain, Biol. Cybernetics, 1970, vol. 7, pp. 129–153.

    Google Scholar 

  25. Artur, B.D.E. and Anton, Z., The Physics of Quantum Information: Quantum Cryptography, Quantum Teleportation, Quantum Computation, Oxford: Springer Publishing Company, 2000.

    MATH  Google Scholar 

  26. Tegmark, M., Importance of Quantum Decoherence in Brain Processes, Phys. Rev., Ser. E, 2000, vol. 61, pp. 4194–4206.

    Article  Google Scholar 

  27. Koch, C. and Hepp, K., Quantum Mechanics in the brain, Nature, 2006, vol. 440, pp. 611–612.

    Article  Google Scholar 

  28. Wansapura, J.P., Holland, S.K., Dunn, R.S., and Ball, W.S., NMR Relaxation Times in the Human Brain at 3.0 Tesla, J. Magn. Reson. Im., 1999, vol. 9, pp. 531–538.

    Article  Google Scholar 

  29. Yin, J., Feix, J., and Hyde, J., Map** of Collision Frequencies for Stearic Acid Spin Labels by Saturation-Recovery Electron Paramagnetic Resonance, Biophys. J., 1990, vol. 58, pp. 713–720.

    Article  Google Scholar 

  30. Okazaki, M., Kuwata, K., Miki, Y., Shiga, S., and Shiga, T., Electron Spin Relaxation of Synthetic Melanin and Melanin-Containing Human Tissues As Studied By Electron Spin Echo and Electron Spin Resonance, Arch. Biochem. Biophys., 1985, vol. 242, pp. 197–205.

    Article  Google Scholar 

  31. Leibold, C. and Kempter, R., Sparseness Constrains the Prolongation of Memory Lifetime Via Synaptic Metaplasticity, Cereb. Cortex, 2008, vol. 18, pp. 67–77.

    Article  Google Scholar 

  32. Gabor, D., Holographic Model of Temporal recall, Nature, 1968, vol. 217, p. 584.

    Article  Google Scholar 

  33. Gabor, D., Improved Holographic Model of Temporal Recall, Nature, 1968, vol. 217, pp. 1288–1289.

    Article  Google Scholar 

  34. Gabor, D., Associative Holographic Memories, IBM J. Res. Dev., 1969, vol. 13, pp. 156–159.

    Article  Google Scholar 

  35. Pribram, K.H., Introduction to Fourier Optics, N.J., Hillsdale: Lawrence Erlbaum Associates, 1991.

    Google Scholar 

  36. Lashley, K.S., Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain, New York: Hafner Pub. Co., 1964.

    Google Scholar 

  37. Hill, B., Some Aspects of a Large Capacity Holographic Memory, Appl. Opt., 1972, vol. 11, pp. 182–191.

    Article  Google Scholar 

  38. Longuet-Higgins, H.C., Holographic Model of Temporal Recall, Nature, 1968, vol. 217, p. 104.

    Article  Google Scholar 

  39. Psaltis, D., Brady, D., Gu, X.-G., and Lin, S., Holography in Artificial Neural Networks, Nature, 1990, vol. 343, pp. 325–330.

    Article  Google Scholar 

  40. Ebersole, J., Optical Image Subtraction, Opt. Eng., 1975, vol. 14, pp. 436–447.

    Article  Google Scholar 

  41. Goodman, J.W., Introduction to Fourier Optics, 3rd ed., Greenwood Village USA: Roberts and Company Publishers, 2005.

    Google Scholar 

  42. Gabor, D., Stroke, G.W., Restrick, R., Funkhouser, A., and Brumm, D., Optical Image Synthesis (Complex Amplitude Addition and Subtraction) by Holographic Fourier Transformation, Phys. Lett., 1965, vol. 18, pp. 116–118.

    Article  Google Scholar 

  43. Lee, S.H., Yao, S.K., and Milnes, A.G., Optical Image Synthesis (Complex Amplitude Addition and Subtraction) in Real Time by a Diffraction-Grating Interferometric Method, J. Opt. Soc. Am., 1970, vol. 60, pp. 1037–1041.

    Article  Google Scholar 

  44. Zhang, H., Hubbard, P.L., Parker, G.J., and Alexander, D.C., Axon Diameter Map** in the Presence of Orientation Dispersion with Diffusion MRI, NeuroImage, 2011, vol. 56, pp. 1301–1315.

    Article  Google Scholar 

  45. Hoshino, T., Banerjee, S., Itoh, M., and Yatagai, T., Design of a Wavelength Independent Grating in the Resonance Domain, Appl. Opt., 2007, vol. 46, pp. 7948–7962.

    Google Scholar 

  46. Born, M. and Wolf, E., Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, 5th ed., New York, Oxford: Pergamon Press, 1975.

    Google Scholar 

  47. E. Hecht, Optics, 4th ed., San Francisco: Addison-Wesley, 2002.

    Google Scholar 

  48. DiCarlo, J.J., Zoccolan, D., and Rust, N.C., How Does the Brain Solve Visual Object Recognition?, Neuron., 2012, vol. 73, pp. 415–434.

    Article  Google Scholar 

  49. Gross, C.G., Genealogy of the Grandmother Cell, Neuroscientist, 2002, vol. 8, pp. 512–518.

    Article  Google Scholar 

  50. Wriedt, T., A Review of Elastic Light Scattering Theories, Part. Part. Syst. Char., 1998, vol. 15, pp. 67–74.

    Article  Google Scholar 

  51. Bohren, C.F. and Huffman, D.R., Absorption and Scattering of Light by Small Particles, New York: Wiley, 1983.

    Google Scholar 

  52. Lalanne, P. and Silberstein, E., Fourier-Modal Methods Applied to Waveguide Computational Problems, Opt. Lett., 2000, vol. 25, pp. 1092–1094.

    Article  Google Scholar 

  53. Hariharan, P., Basics of Holography, Cambridge: Cambridge University Press, 2002.

    Book  Google Scholar 

  54. Tanaka, T. and Kawata, S., Comparison of Recording Densities in Three-Dimensional Optical Storage Systems: Multilayered Bit Recording Versus Angularly Multiplexed Holographic Recording, J. Opt. Soc. Am., Ser. A, 1996, vol. 13, pp. 935–942.

    Article  Google Scholar 

  55. Gu, M. and Li, X., The Road to Bit-By-Bit Optical Data Storage, OPN Opt. Photon. News, 2010, vol. 21, pp. 29–33.

    Article  Google Scholar 

  56. Hecht, E., Optics, 4th ed., San Francisco: Addison-Wesley, 2002.

    Google Scholar 

  57. Masters, B.R., Ernst Abbe and the Foundation of Scientific Microscopes, Opt. Photon. News, 2007, vol. 18, pp. 18–23.

    Article  Google Scholar 

  58. Patterson, K., Nestor, P.J., and Rogers, T.T., Where Do You Know What You Know? The Representation of Semantic Knowledge in the Human Brain, Nature Rev. Neurosci., 2007, vol. 8, pp. 976–988.

    Article  Google Scholar 

  59. Squire, L.R., Wixted, J.T., and Clark, R.E., Recognition Memory and the Medial Temporal Lobe: a New Perspective, Nature Rev. Neurosci., 2007, vol. 8, pp. 872–883.

    Article  Google Scholar 

  60. Bird, C.M. and Burgess, N., The Hippocampus and Memory: Insights from Spatial Processing, Nature Rev. Neurosci., 2008, vol. 9, pp. 182–194.

    Article  Google Scholar 

  61. Cabeza, R., Ciaramelli, E., Olson, I.R., and Moscovitch, M., The Parietal Cortex and Episodic Memory: an Attentional Account, Nature Rev. Neurosci., 2008, vol. 9, pp. 613–625.

    Article  Google Scholar 

  62. Takeuchi, D., Hirabayashi, T., Tamura, K., and Miyashita, Y., Reversal of Interlaminar Signal between Sensory and Memory Processing in Monkey Temporal Cortex., Science, 2011, vol. 331, pp. 1443–1447.

    Article  Google Scholar 

  63. Miyashita, Y., Cognitive Memory: Cellular and Network Machineries and Their Top-Down Control., Science, 2004, vol. 306, pp. 435–440.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Applied Physics, University of Tsukuba, Tsukuba, Japan

    Tetsuya Hoshino & Masahide Itoh

  2. Center for Optical Research and Education, Utsunomiya University, Utsunomiya, Japan

    Toyohiko Yatagai

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  1. Tetsuya Hoshino
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  2. Toyohiko Yatagai
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  3. Masahide Itoh
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Correspondence to Tetsuya Hoshino.

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Hoshino, T., Yatagai, T. & Itoh, M. A columnar model explaining long-term memory. Opt. Mem. Neural Networks 21, 209–218 (2012). https://doi.org/10.3103/S1060992X12040042

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