A A Spiking Model of Cell Assemblies: Short Term and Associative Memory
Main Article Content
Abstract
Cell Assemblies are the neural basis of both long and short term memories. Cell Assemblies, whose neurons persistently fire, are active short term memories while the neurons are firing, and the memory ceases to be active when the neurons stop firing. This paper provides simulations of excitatory spiking neurons with small world topologies that persist for several hundred milliseconds. Extending this model to include short term depression allows the Cell Assemblies to persist for several seconds, a reasonable psychological duration. These Cell Assemblies are combined in a simple associative memory so that when three Cell Assemblies are associated, ignition of two causes the third to ignite, while pairs of un-associated Cell Assemblies do not lead to the ignition of other Cell Assemblies. This mechanism has a larger capacity than a Hopfield net. The simulations provide a simple neurally based simulation model of short term and associative memory. A discussion of the current psychological theories, other mechanisms for short term memory, the strengths and weaknesses of the paper's simulated models, and proposed challenges are also provided.
Keywords:
Cell assemblies, Spiking Neuron, Short Term Memory, Associative Memory, Short Term Depression
Article Details
How to Cite
HUYCK, Christian.
A A Spiking Model of Cell Assemblies: Short Term and Associative Memory.
Medical Research Archives, [S.l.], v. 11, n. 9, sep. 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4406>. Date accessed: 21 dec. 2024.
doi: https://doi.org/10.18103/mra.v11i9.4406.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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66. R. Lewis and S. Vasishth, "An activation-based model of sentence processing as skilled memory retrieval," Cognitive Science, pp. 375-419, 2005.
2. W. Singer, A. Engel, A. Kreiter, M. Munk, S. Neuenschwander and P. Roelfsema, "Neuronal assemblies: necessity, signature and detectability," Trends in Cognitive Sciences, pp. 252-261, 1997.
3. K. Harris, "Neural signatures of cell assembly organization," Nature Reviews Neuroscience, pp. 399-407, 2005.
4. G. Dragoi and G. Buzaski, "Temporal Encoding of Place Sequences in Hippocampal Cell Assemblies," Neuron, pp. 145-157, 2006.
5. F. Zenke, A. Agnes and W. Gerstner, "Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks},," Nature Communications, p. 6922, 2015.
6. C. Huyck and I. Mitchell, "CABots and Other Neural Agents," Frontiers in Neurorobotics, p. 79, 2018.
7. J. Anderson, D. Bothell, M. Byrne, S. Douglass, C. Lebiere and Y. Qin, "An integrated theory of the mind," Psychological review, pp. 1036-60, 2004.
8. C. Tetzlaff, S. Dasgupta, T. Kulvicius and W. Florentin, "The use of Hebbian cell assemblies for nonlinear computation," Scientific reports, p. 12866, 2015.
9. R. Kreiser, M. Cartiglia, J.Martel, J. Conradt and Y. Sandamirskaya, "A neuromorphic approach to path integration: a head-direction spiking neural network with vision-driven reset," in IEEE International Symposium on Circuits and Systems (ISCAS), 2018.
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11. F. Fiebig and A. Lansner, "A spiking working memory model based on Hebbian short-term potentiation," Journal of Neuroscience, pp. 83-96, 2017.
12. S. Kaplan, M. Weaver and R. French, "Active Symbols and Internal Models: Towards a Cognitive Connectionism," Connection Science, pp. 51-71, 1990.
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14. N. Cowan, "What are the differences between long-term, short-term, and working memory?," Progress in brain research, pp. 323-338, 2008.
15. P. Van DenBroek, D. Rapp and P. Kendeou, "Integrating memory-based and constructionist processes in accounts of reading comprehension," Discourse processes, pp. 299-316, 2005.
16. S. Kaplan, M. Sontag and E. Chown, "Tracing recurrent activity in cognitive elements (TRACE): A model of temporal dynamics in a cell assembly," Connection Science, pp. 179-206, 1991.
17. P. deVries and K. vanSlochteren, "The Nature of the Memory Trace and its Neurocomputational Implications," Computational Neuroscience, pp. 188-202, 2008.
18. Y. Ikegaya, G. Aaron, R. Cossart, D. Aronov, I. Lampl, D. Ferster and R. Yuste, "Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity," Science, pp. 559-564, 2004.
19. J. Hopfield, "Neural Nets and Physical Systems with Emergent Collective Computational Abilities," PNAS, pp. 2554-2558, 1982.
20. A. Hodgkin and A. Huxley, "A quantitative description of membrane current and its application to conduction and excitation in nerve," Journal of Physiology, pp. 500-544, 1952.
21. E. Izhikevich, "Which Model to Use for Cortical Spiking Neurons?," IEEE Transactions on Neural Networks, pp. 1063-1070, 2004.
22. R. Brette and W. Gerstner, "Adaptive exponential integrate-and-fire model as an effective description of neuronal activity," Journal of Neurophysiology, vol. 94, pp. 3637-3642, 2005.
23. R. Brette, M. Rudolph, T. Carnevale, M. Hines, D. Beeman, J. Bower, M. Diesmann, A. Morrison and A. Destexhe, "Simulation of networks of spiking neurons: A review of tools and strategies," Journal of Computational Neuroscience, pp. 349-398, 2007.
24. N. Fourcaud-Trocmé, D. Hansel, C. Van Vreeswijk and N. Brunel, "How spike generation mechanisms determine the neuronal response to fluctuating inputs," Journal of neuroscience, pp. 11628-11640, 2003.
25. A. Davison, D. Bruderle, J. Eppler, E. Muller, D. Pecevski, L. Perrinet and P. Yqer, "{PyNN}: a common interface for neuronal network simulators," Frontiers in Neuroinformatics, p. 2, 2008.
26. M. Gewaltig and M. Diesmann, "NEST (NEural Simulation Tool)," Scholarpedia, 2007.
27. J. Benda, "Neural adaptation," Current Biology, pp. R110-R116, 2021.
28. C. Van Vreeswijk, G. Ermentrout and L. Abbott, "When inhibition not excitation synchronizes neural firing," Journal of computational neuroscience, pp. 313-321, 1994.
29. A. Zeisel, A. Muñoz-Manchado, S. Codeluppi, P. Lönnerberg, G. L. Manno and S. Linnarsson, "Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq," Science, pp. 1138-1142, 2015.
30. P. Churchland and T. Sejnowski, The Computational Brain, MIT Press, 1999.
31. T. Trappenberg, Fundamentals of Computational Neuroscience, Oxford: Oxford Press, 2010.
32. J. Bohland and A. Minai, "Efficient Associative Memory using Small-World Architecture," Neurocomputing, pp. 489-496, 2001.
33. L. Zemanová, C. Zhou and J. Kurths, "Structural and functional clusters of complex brain networks," Physica D: Nonlinear Phenomena, pp. 202-212, 2006.
34. C. Zhou, L. Zemanova, G. Zamora, J. Hilgetag and J. Kurths, "Hierarchical organization unveiled by functional connectivity in complex brain networks," Physical review letters, p. 238103, 2006.
35. R. Cancho and R. Solé, "The small world of human language," Proceedings of the Royal Society of London. Series B: Biological Sciences, pp. 2261-2265, 2001.
36. P. beimGraben and J. Kurths, "Simulating global properties of electroencephalograms with minimal random neural networks," Neurocomputing, pp. 999-1007, 1999.
37. J. Downes, M. Hammond, D. Xydas, M. Spencer, V. Becerra, K. Warwick, B. Whalley and S. Nasuto, "Emergence of a small-world functional network in cultured neurons," PLoS computational biology, p. e1002522, 2012.
38. L. Abbott, A. Verela, K. Sen and S. Nelson, "Synaptic Depression and Cortical Gain Control," Science, pp. 220-224, 1997.
39. R. Zucker and W. Regher, "Short-Term Synaptic Plasticity," Annual Review of Physiology, pp. 355-405, 2002.
40. M. Quillian, "Word concepts: A theory of simulation of some basic semantic capabilities," Behavioral Science, pp. 410-430, 1967.
41. E. Mizraji, "Context-dependent associations in linear distributed memories," Bulletin of mathematical biology, pp. 195-205, 1989.
42. E. Mizraji and J. Lin, "Logic in a dynamic brain," Bulletin of mathematical biology, pp. 373-397, 2011.
43. C. Huyck, "A Psycholinguistic Model of Natural Language Parsing Implemented in Simulated Neurons," Cognitive Neurodynamics, pp. 316-330, 2009.
44. Y. Fan and C. Huyck, "Implementation of Finite State Automata using fLIF Neurons," in IEEE Systems, Man and Cybernetics Society, London, 2008.
45. T. Wennekers, M. Garagnani and F. Pulvermuller, "Language models based on Hebbian cell assemblies," Journal of Phsyiology-Paris, pp. 16-30, 2006.
46. D. Kahneman, Thinking, fast and slow, Macmillan, 2011.
47. M. Rabinovich and P. Varona, "Discrete sequential information coding: heteroclinic cognitive dynamics," Frontiers in Computational Neuroscience, p. 73, 2018.
48. J. Fuster and G. Alexander, "Neuron Activity Related to Short-Term Memory," Science, pp. 652-654, 1971.
49. O. Barak and M. Tsodyks, "Working models of working memory," Current opinion in neurobiology, pp. 20-24, 2014.
50. M. Boerlin, C. Machens and S. Denève, "Predictive coding of dynamical variables in balanced spiking networks," PLoS computational biology, p. e1003258, 2013.
51. L. Abbott, B. DePasquale and R. Memmesheimer, "Building functional networks of spiking model neurons," Nature Neuroscience, pp. 350-355, 2016.
52. E. Miller, M. Lundqvist and A. Bastos, "Working Memory 2.0," Neuron, pp. 463-475, 2018.
53. F. Randi and A. Leifer, "Measuring and modeling whole-brain neural dynamics in Caenorhabditis elegans," Current Opinion in Neurobiology, pp. 167-175, 2020.
54. H. Markram, E. Muller, S. Ramaswamy, M. Reimann, M. Abdellah, C. Sanchez and F. Schürmann, "Reconstruction and simulation of neocortical microcircuitry," cell, pp. 456-492, 2015.
55. S. Furber, D. Lester, L. Plana, J. Garside, E. Painkras, S. Temple and A. Brown, "Overview of the SpiNNaker system architecture," IEEE Transactions on Computers, pp. 2454-2467, 2013.
56. F. Melozzi, M. Woodman, V. Jirsa and C. Bernard, "The virtual mouse brain: a computational neuroinformatics platform to study whole mouse brain dynamics," Eneuro, pp. 456-492, 2017.
57. T. Knowles, R. Stentiford and M. Pearson, "WhiskEye: A Biomimetic Model of Multisensory Spatial Memory Based on Sensory Reconstruction," in Conference Towards Autonomous Robotic Systems, 2021.
58. L. Trudeau and R. Gutiérrez, "On cotransmission and neurotransmitter phenotype plasticity," Molecular Interventions, pp. 138-146, 2007.
59. G. Bi and M. Poo, "Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type," Journal of Neuroscience, pp. 10464-10472, 1998.
60. E. Oja, "A Simplified Neuron Model as a Principal Component Analyzer," Journal of Mathematical Biology, pp. 267-273, 1982.
61. M. Butz, F. Woergoetter and A. vanOoyen, "Activity-dependent structural plasticity," Brain research reviews, pp. 287-305, 2009.
62. C. von der Malsburg, "Toward understanding the neural code of the brain," Biological Cybernetics, pp. 439-449, 2021.
63. A. Stocco, C. Sibert, Z. Steine-Hanson, N. Koh, J. Laird, C. Lebiere and P. Rosenbloom, "Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains," NeuroImage, p. 118035, 2021.
64. E. Stern, D. Jaeger and C. Wilson, "Membrane potential synchrony of simultaneously recorded striatal spiny neurons in vivo," Nature, pp. 475-478, 1998.
65. L. Carrillo-Reid, F. Tecuapetla, D. Tapia, A. Hernández-Cruz, E. Galarraga, R. Drucker-Colin and J. Bargas, "Encoding network states by striatal cell assemblies," Journal of neurophysiology, pp. 1435-1450, 2008.
66. R. Lewis and S. Vasishth, "An activation-based model of sentence processing as skilled memory retrieval," Cognitive Science, pp. 375-419, 2005.