Relativistic Quantum Decryption of Large-Scale Neural Coding  arXiv-ed 

Throughout man’s never-ceasing endeavor to understand one of the most convoluted systems in nature, it is wise to be reminded of incompleteness, in Gödel’s sense. All the same, rather than denying progress beyond classical models, we may bring to the light of day the foresight that quantum mind, brain and consciousness are explorable without recurring to mysticism. A relativistic quantum approach to the decryption of large-scale neural coding is herein conceived to shed light on early keystone architectures of multidimensional synaptic economics and to guide the development of quantum artificial intelligence. 

A spin-geometrical construction is unveiled, which, while conditionally intimating matter-antimatter asymmetry, is leveraged to compensate for certain challenges posed to classical approaches by the emergent needs to construe large-scale neural recordings linking brain and spontaneous behavior. Conversely, by a technical treatment applicable to the field of neuroscience and beyond, a link between instantiations of self-duality, mirror supersymmetry and relativistic quantum theory is principally drawn, adding new dimensions to previous observations. 

Within a graphical game-theoretical framework – emphasizing the role of inhibition in deeply clustered networks – the approach is conducive to the high-precision reproduction, reinterpretation and guidance of experimental outcomes on neural coding in the self-organizing brain, in connection with behavior and geometric dimensionality. And to octonionic dynamics sustained by the esoteric structure of triality, as candidate dynamics underlying internal states or emotions. 

Principles of (hyper-)self-duality under (hyper-)mirror supersymmetry, evoking relativistic quantum superposition and entanglement, suggest that (i) the instantaneous geometric dimensionality of neural (co-)representations of a spontaneous (co-)behavioral state is at most 16, in the same direction of (co-)flow; (ii) the respective visible and ‘dark’ neural coding scenarios are coexistable and sustainable by highly symmetric structures, dual to each other, with Lorentz (co-)crystals being characteristic of these structures.

Neural Crystals arXiv-ed

We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. 

The designs draw on a self-dual classical description – via special Clifford-Lipschitz operations – of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally ‘trained’, Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. 

The approach accommodates the fusion of up to 16 time-invariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.