Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
134 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Modeling of Memory Mechanisms in Cerebral Cortex and Simulation of Storage Performance (2401.00381v2)

Published 31 Dec 2023 in q-bio.NC and cs.DC

Abstract: At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and autonomous behavioral patterns. They rely on algorithms with a global view, significantly differing from biological neural networks, in which, to simulate information storage and retrieval processes, the limitations of centralized algorithms must be overcome. This study introduces a directed graph model that equips each node with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information storage and modeling and simulation of the brain's memory process. We abstract different storage instances as directed graph paths, transforming the storage of information into the assignment, discrimination, and extraction of different paths. To address writing and reading challenges, each node has a personalized adaptive learning ability. A storage algorithm without a God's eye view is developed, where each node uses its limited neighborhood information to facilitate the extension, formation, solidification, and awakening of directed graph paths, achieving competitive, reciprocal, and sustainable utilization of limited resources. Storage behavior occurs in each node, with adaptive learning behaviors of nodes concretized in a microcircuit centered around a variable resistor, simulating the electrophysiological behavior of neurons. Under the constraints of neurobiology on the anatomy and electrophysiology of biological neural networks, this model offers a plausible explanation for the mechanism of memory realization, providing a comprehensive, system-level experimental validation of the memory trace theory.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (53)
  1. doi:https://doi.org/10.1038/nn.4237.
  2. doi:https://doi.org/10.1146/annurev.neuro.23.1.649.
  3. doi:https://doi.org/10.1038/s41539-019-0048-y.
  4. doi:https://doi.org/10.1038/s41467-021-24269-4.
  5. doi:https://doi.org/10.1038/s41593-019-0480-6.
  6. doi:https://doi.org/10.1016/j.celrep.2020.02.026.
  7. doi:https://doi.org/10.1038/s41386-019-0490-9.
  8. doi:https://doi.org/10.1002/anie.202200716.
  9. doi:https://doi.org/10.1002/aisy.202100058.
  10. doi:https://doi.org/10.1038/s41593-018-0210-5.
  11. doi:https://doi.org/10.1126/science.aaw4325.
  12. doi:https://doi.org/10.1515/9781400841103.
  13. doi:https://doi.org/10.1109/TNNLS.2021.3070843.
  14. doi:https://doi.org/10.1109/SocialCom.2013.106.
  15. doi:https://doi.org/10.1145/1322432.1322433.
  16. doi:https://doi.org/10.1109/TKDE.2020.2981333.
  17. doi:https://doi.org/10.1007/s00521-022-07862-6.
  18. doi:https://doi.org/10.1109/TAI.2022.3194869.
  19. doi:https://doi.org/10.1109/ACCESS.2018.2831228.
  20. doi:https://doi.org/10.1016/j.engappai.2011.09.025.
  21. doi:https://doi.org/10.11591/ijece.v12i4.pp3517-3529.
  22. doi:https://doi.org/10.1109/ICRA.2018.8461113.
  23. doi:https://doi.org/10.1073/pnas.79.8.2554.
  24. doi:https://doi.org/10.1016/j.neucom.2017.02.037.
  25. doi:https://doi.org/10.1109/TNNLS.2013.2268542.
  26. doi:https://doi.org/10.1109/SSCI.2017.8285215.
  27. doi:https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.18.
  28. doi:https://doi.org/10.1007/978-3-030-30487-4_5.
  29. doi:https://doi.org/10.1109/21.87054.
  30. doi:https://doi.org/10.1016/j.neucom.2020.11.023.
  31. doi:https://doi.org/10.1109/TSMC.2020.3043249.
  32. doi:https://doi.org/10.1016/j.asoc.2017.08.026.
  33. doi:https://doi.org/10.1109/IJCNN.2019.8852089.
  34. doi:https://doi.org/10.7554/eLife.23763.
  35. doi:https://doi.org/10.1007/s11704-007-0035-y.
  36. doi:https://doi.org/10.1016/S0893-6080(03)00137-0.
  37. doi:https://doi.org/10.1109/IJCNN.2005.1556020.
  38. doi:https://doi.org/10.3389/fnins.2019.00650.
  39. doi:https://doi.org/10.1162/neco_a_01181.
  40. doi:https://doi.org/10.1609/aaai.v31i1.10515.
  41. doi:https://doi.org/10.1109/MSP.2010.939537.
  42. doi:https://doi.org/10.1109/TNN.2011.2146789.
  43. doi:https://doi.org/10.1109/TNNLS.2015.2431319.
  44. doi:https://doi.org/10.1162/neco_a_01417.
  45. doi:https://doi.org/10.1109/TCYB.2019.2951520.
  46. doi:https://doi.org/10.1038/453042a.
  47. doi:https://doi.org/10.1142/S0218127408022354.
  48. doi:https://doi.org/10.1109/TCYB.2014.2350977.
  49. doi:https://doi.org/10.1109/TETCI.2019.2921787.
  50. doi:https://doi.org/10.1016/j.neucom.2018.11.050.
  51. doi:https://doi.org/10.1016/j.amc.2017.11.037.
  52. doi:https://doi.org/10.1109/TIT.1987.105732.
  53. doi:https://doi.org/10.1126/science.abm0204.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets