Resonance Complexity Theory and the Architecture of Consciousness: A Field-Theoretic Model of Resonant Interference and Emergent Awareness
This paper introduces Resonance Complexity Theory (RCT), which proposes that consciousness emerges from stable interference patterns of oscillatory neural activity. These patterns, shaped by recursive feedback and constructive interference, must exceed critical thresholds in complexity, coherence, gain, and fractal dimensionality to give rise to conscious experience. The resulting spatiotemporal attractors encode subjective awareness as dynamic resonance structures distributed across the neural field, enabling large-scale integration without symbolic representation or centralized control. To formalize this idea, we define the Complexity Index (CI), a composite metric that synthesizes four core properties of conscious systems: fractal dimensionality (D), signal gain (G), spatial coherence (C), and attractor dwell time (tau). These elements are combined multiplicatively to capture the emergence and persistence of structured, integrative neural states. To test the theory empirically, we developed a biologically inspired yet minimal neural field simulation composed of radial wave sources emitting across a continuous 2D space. The system exhibits recursive constructive interference, producing coherent, attractor-like excitation patterns without external input, regional coding, or imposed structure. These patterns meet the theoretical thresholds for CI and reflect the core dynamics predicted by RCT. The findings demonstrate that resonance-based attractors -- and by extension, consciousness-like dynamics -- can arise purely from the physics of wave interference. RCT thus offers a unified, dynamical framework for modeling awareness as an emergent property of organized complexity in oscillatory systems.
Code (1)
Similar Papers 제목 키워드 기반
A Machine Consciousness architecture based on Deep Learning and Gaussian Processes
Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep learning or generative models have achiev…
Common Sense ReasoningDeep LearningGaussian ProcessesLarge-scale signatures of unconsciousness are consistent with a departure from critical dynamics
Loss of cortical integration and changes in the dynamics of electrophysiological brain signals characterize the transition from wakefulness towards unconsciousness. The common mechanism underlying these observations rema…
AnatomyEfficient ExplorationFunctional ConnectivityA Theory of Consciousness from a Theoretical Computer Science Perspective: Insights from the Conscious Turing Machine
The quest to understand consciousness, once the purview of philosophers and theologians, is now actively pursued by scientists of many stripes. We examine consciousness from the perspective of theoretical computer scienc…
Falsification and consciousness
The search for a scientific theory of consciousness should result in theories that are falsifiable. However, here we show that falsification is especially problematic for theories of consciousness. We formally describe t…
PredictionNon-separability of Physical Systems as a Foundation of Consciousness
A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-s…