1. Complex Recurrent Spectral Network.
- Author
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Chicchi, Lorenzo, Giambagli, Lorenzo, Buffoni, Lorenzo, Marino, Raffaele, and Fanelli, Duccio
- Subjects
- *
ARTIFICIAL neural networks , *ARTIFICIAL intelligence , *INFORMATION processing , *EIGENVALUES - 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). • The C-RSN overcomes some limitations in existing neural network models. • C-RSN introduces localized non-linearity, complex eigenvalues and a separated memory. • The network evolves towards a dynamic, oscillating final state. • The model's efficacy is demonstrated through empirical evaluation. • C-RSN is able to process sequential inputs keeping track of the insertion order. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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