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Complex Recurrent Spectral Network

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  • Chicchi, Lorenzo
  • Giambagli, Lorenzo
  • Buffoni, Lorenzo
  • Marino, Raffaele
  • Fanelli, Duccio

Abstract

This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network (ℂ-RSN), an innovative variant of the Recurrent Spectral Network (RSN) model. The ℂ-RSN model introduces localized non-linearity, complex fixed eigenvalues, and a distinct separation of memory and input processing functionalities. These features enable the ℂ-RSN to evolve towards a dynamic, oscillating final state that bear some degree of similarity with biological cognition. The model’s ability to classify data through a time-dependent function, and the localization of information processing, is demonstrated by using the MNIST dataset. Remarkably, distinct items supplied as a sequential input yield patterns in time which bear the indirect imprint of the insertion order (and of the separation in time between contiguous insertions).

Suggested Citation

  • Chicchi, Lorenzo & Giambagli, Lorenzo & Buffoni, Lorenzo & Marino, Raffaele & Fanelli, Duccio, 2024. "Complex Recurrent Spectral Network," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924005502
    DOI: 10.1016/j.chaos.2024.114998
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    References listed on IDEAS

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    1. P. Baldi & P. Sadowski & D. Whiteson, 2014. "Searching for exotic particles in high-energy physics with deep learning," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    2. Chicchi, Lorenzo & Fanelli, Duccio & Giambagli, Lorenzo & Buffoni, Lorenzo & Carletti, Timoteo, 2023. "Recurrent Spectral Network (RSN): Shaping a discrete map to reach automated classification," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Lorenzo Giambagli & Lorenzo Buffoni & Timoteo Carletti & Walter Nocentini & Duccio Fanelli, 2021. "Machine learning in spectral domain," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    4. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
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