Delay Coordinate Embedding as Neuronally Implemented Information Processing: The State Space Theory of Consciousness

The state space theory of consciousness proposes that the cortex processes information by using recurrent neural network engines to embed sensory input into a state space, following Takens' theorem. Consciousness arises at the highest-order engines within a hierarchy of parallel pathways, making it a dynamic process rather than a static neuronal state. This reconciles dualist intuitions with a monist perspective. Each individual's neuronal representations develop uniquely due to history-dependent training, explaining the privacy of qualia, cortical plasticity, and heuristic processing. The theory's non-linear dynamics, sensitive to initial conditions, account for ambiguous figure interpretation and suggest a pathway to free will. It aligns with and expands upon higher-order theories, global workspace theories, and integrated information theory by providing a unifying computational mechanism.

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Abstract

This paper introduces the state space theory of consciousness, positing that the cortex processes information through delay coordinate embedding operationalized by recurrent neural network engines. This leverages the power of Takens' theorem, giving rise to representations of reality as points within state space. Consciousness is posited to arise at the highest order engines amongst hierarchical and parallel engine pathways. Consciousness is cast as a dynamic process rather than as a neuronal state, reconciling dualist intuitions with a monist perspective. Neuronal representations develop uniquely in each individual due to history-dependent training of these engines, accounting for the privacy of qualia while also addressing cortical plasticity and the heuristic nature of cortical processing. Posited engines exhibit non-linear dynamics that are sensitive to initial conditions, explaining phenomena such as ambiguous figure interpretation and offering a pathway to explaining free will. The state space theory aligns with and expands upon major theories (e.g.higher-order theories, global workspace theories, integrated information theory), essentially providing a computational mechanism that unifies elements of these theories. Future work will explore neural mechanisms and validate predictions.