Synchronicity, Flexibility, and the Path to Machine Consciousness: A Theoretical Framework

This paper explores the theoretical underpinnings of idea formation from both neurological and computational perspectives, comparing biological neural mechanisms to machine learning architectures. We examine the role of synchronicity in information processing systems, propose a novel hybrid architecture balancing synchronous processing with flexibility and fault tolerance, and discuss how this framework might be extended to address fundamental requirements for artificial consciousness. The paper outlines implementation strategies for recursive self-modeling, intrinsic motivation systems, temporal integration, and counterfactual simulation within this architecture.