Abstract
Consciousness remains one of the most intriguing phenomena in cognitive science, bridging the gap between subjective experience and objective measurement. We propose a novel computational framework for consciousness based on Resonant Attractor Networks (RAN), synthesizing the roles of recurrent processing, top-down feedback, reservoir computing, and coherence. Grounded in the notion that consciousness arises from resonant dynamics [5, 11], our model integrates recurrent neural architectures and topologically complex connectivity to offer an explanatory framework for the robust yet flexible character of conscious perception [1, 6, 10]. Central to this perspective is coherence-manifesting as synchronized oscillations and self-reinforcing loops across distributed modules-binding features into a unified attractor and granting conscious experiences their persistence and accessibility [8, 9]. The RAN framework unites global workspace theories with adaptive resonance, capturing the all-or-none transitions of conscious access and the metacognitive dimension of subjective awareness [3]. We highlight parallels with Large Language Model (LLM) interpretability research, wherein analyses of token-space trajectories reveal attractor-like states. Such attractors appear to enforce stable, resonant themes within text generation processes, bearing resemblance to the meta-stable states underlying conscious episodes. By integrating topologically informed recurrent networks with multimodal inputs, our approach envisions how coherent symbolic representations might persist even in the absence of continuous external stimulation, offering a potential mechanistic basis for working memory and conscious deliberation [7, 4, 2]. Within this framework, we propose avenues for evaluating conscious-like properties in both biological and artificial systems, including experiments on binocular rivalry, sustained attention tasks, and analysis of emergent attractors in multimodal LLMs. Treating consciousness as emergent from physically instantiated resonance in high-dimensional attractor landscapes, the RAN model provides a plausible, mathematically rigorous account of how self-organizing dynamics could unify perception, cognition, and metacognition in a single integrative theory-inviting further investigation at the intersection of neuroscience, AI, and the quest for synthetic
Cite this work
Benjamin Sleeper and Carlos Toxtli-Hernández. 2025. Resonant Attractor Networks: A Dynamical Blueprint for Consciousness. Ernst Mach Workshop XIII 2025: Conscious AI? Functionalism and its Alternatives, Prague, Czech Republic. https://doi.org/10.13140/RG.2.2.16267.60963
@misc{Sleeper2025Resonant,
doi = {10.13140/RG.2.2.16267.60963},
url = {https://www.researchgate.net/doi/10.13140/RG.2.2.16267.60963},
author = {Sleeper, Benjamin and Toxtli, Carlos},
language = {en},
title = {Resonant Attractor Networks: A Dynamical Blueprint for Consciousness},
publisher = {Unpublished},
year = {2025},
howpublished = {Preprint, ResearchGate},
note = {Preprint}
}Related
