IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v184y2024ics0960077924005502.html
   My bibliography  Save this article

Complex Recurrent Spectral Network

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077924005502
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2024.114998?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Da Liu & Ming Xu & Dongxiao Niu & Shoukai Wang & Sai Liang, 2016. "Forecast Modelling via Variations in Binary Image-Encoded Information Exploited by Deep Learning Neural Networks," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-9, June.
    2. Kevin Fauvel & Tao Lin & Véronique Masson & Élisa Fromont & Alexandre Termier, 2021. "XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification," Mathematics, MDPI, vol. 9(23), pages 1-19, December.
    3. Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
    4. Wang, Jia & Hu, Jun & Shen, Shifei & Zhuang, Jun & Ni, Shunjiang, 2020. "Crime risk analysis through big data algorithm with urban metrics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. Parmar, Janak & Das, Pritikana & Dave, Sanjaykumar M., 2021. "A machine learning approach for modelling parking duration in urban land-use," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
    7. Sherwan Mohammed Najm & Imre Paniti, 2023. "Investigation and machine learning-based prediction of parametric effects of single point incremental forming on pillow effect and wall profile of AlMn1Mg1 aluminum alloy sheets," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 331-367, January.
    8. Vaishali U. Gongane & Mousami V. Munot & Alwin D. Anuse, 2024. "A survey of explainable AI techniques for detection of fake news and hate speech on social media platforms," Journal of Computational Social Science, Springer, vol. 7(1), pages 587-623, April.
    9. Baroffio, Andrea & Rotondo, Pietro & Gherardi, Marco, 2024. "Resolution of similar patterns in a solvable model of unsupervised deep learning with structured data," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    10. Davazdahemami, Behrooz & Kalgotra, Pankush & Zolbanin, Hamed M. & Delen, Dursun, 2023. "A developer-oriented recommender model for the app store: A predictive network analytics approach," Journal of Business Research, Elsevier, vol. 158(C).
    11. Dang, Khue-Dung & Quiroz, Matias & Kohn, Robert & Tran, Minh-Ngoc & Villani, Mattias, 2019. "Hamiltonian Monte Carlo with Energy Conserving Subsampling," Working Paper Series 372, Sveriges Riksbank (Central Bank of Sweden).
    12. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.
    13. Kunal Pattanayak & Vikram Krishnamurthy, 2021. "Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification," Papers 2102.04594, arXiv.org, revised Jul 2021.
    14. Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
    15. 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).
    16. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    17. Jerol Soibam & Achref Rabhi & Ioanna Aslanidou & Konstantinos Kyprianidis & Rebei Bel Fdhila, 2020. "Derivation and Uncertainty Quantification of a Data-Driven Subcooled Boiling Model," Energies, MDPI, vol. 13(22), pages 1-30, November.
    18. Mark Gromowski & Michael Siebers & Ute Schmid, 2020. "A process framework for inducing and explaining Datalog theories," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 821-835, December.
    19. Pessa, Arthur A.B. & Zola, Rafael S. & Perc, Matjaž & Ribeiro, Haroldo V., 2022. "Determining liquid crystal properties with ordinal networks and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    20. Fallahgoul, Hasan & Franstianto, Vincentius & Lin, Xin, 2024. "Asset pricing with neural networks: Significance tests," Journal of Econometrics, Elsevier, vol. 238(1).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924005502. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.