Complex Recurrent Spectral Network

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).