Complex Recurrent Spectral Network

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

Chaos, Solitons & Fractals, 2024, vol. 184, issue C

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).

Keywords: Neural networks; Dynamical systems; Discrete maps; Machine learning; Recurrent Networks; Attractors (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924005502

DOI: 10.1016/j.chaos.2024.114998

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