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Role of delay in brain dynamics

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  • Meir, Yuval
  • Tevet, Ofek
  • Tzach, Yarden
  • Hodassman, Shiri
  • Kanter, Ido

Abstract

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and M multiple delays between successive layers, thereby generating a polynomial time-series outputs with M. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with M outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.

Suggested Citation

  • Meir, Yuval & Tevet, Ofek & Tzach, Yarden & Hodassman, Shiri & Kanter, Ido, 2024. "Role of delay in brain dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
  • Handle: RePEc:eee:phsmap:v:654:y:2024:i:c:s0378437124006757
    DOI: 10.1016/j.physa.2024.130166
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    References listed on IDEAS

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    1. Koresh, Ella & Halevi, Tal & Meir, Yuval & Dilmoney, Dolev & Dror, Tamar & Gross, Ronit & Tevet, Ofek & Hodassman, Shiri & Kanter, Ido, 2024. "Scaling in Deep and Shallow Learning Architectures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    2. Tevet, Ofek & Gross, Ronit D. & Hodassman, Shiri & Rogachevsky, Tal & Tzach, Yarden & Meir, Yuval & Kanter, Ido, 2024. "Efficient shallow learning mechanism as an alternative to deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    3. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
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