Nonlinear Time-Delay Networks and Consciousness Simulation
Zenodo (CERN European Organization for Nuclear Research) August 28, 2026 preprint DOI: 10.5281/zenodo.22136790 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Key points | Proposes that nonlinear time-delay networks coupled with reinforcement learning may model fundamental features of consciousness, such as subjective experience and contextual awareness, by autonomously generating complex internal states and behaviors. The paper presents a theoretical framework and architecture without experimental validation. |
Abstract
This paper investigates the potential of nonlinear time-delay networks (NTDNs) in simulating aspects of human consciousness. The core claim is that by constructing networks with intricate nonlinear time-delay dependencies, it may be possible to model fundamental features of consciousness, such as subjective experience and contextual awareness. The proposed mechanism involves designing an NTDN architecture characterized by significant nonlinearities and temporal dependencies, coupled with reinforcement learning. This allows the network to autonomously learn and adapt to its environment, generating complex internal states and behavioral patterns. The novelty lies in exploring the role of these nonlinear time-delay relationships within the context of consciousness simulation and presenting a novel network architecture for this purpose. The research focuses on the theoretical framework and architectural design, omitting experimental validation. The simulation aims to bridge the gap between computational models and our understanding of consciousness.