Relativistic quantum decryption of large-scale neural coding
Sofia Karamintziou

TL;DR
This paper introduces a relativistic quantum framework to analyze large-scale neural coding, revealing multidimensional structures, dark neural states, and potential links to fundamental physics, advancing neuroscience understanding.
Contribution
It presents a novel quantum and geometric approach to neural coding, integrating relativistic principles to interpret complex brain data and neural architectures.
Findings
Neural representations are at most 16-dimensional.
Coexistence of ordinary and dark neural codes.
Potential links between neural dynamics and fundamental physics concepts.
Abstract
Spin-geometrical projections, from the study of the human universe onto the study of the self-organizing brain, are herein leveraged to address certain concerns raised in latest neuroscience research, namely (i) the extent to which neural codes are multidimensional; (ii) the functional role of neural dark matter; (iii) the challenge to classical model frameworks posed by the needs for accurate interpretation of large-scale neural recordings linking brain and behavior. On the grounds of (hyper-)self-duality under (hyper-)mirror supersymmetry, relativistic quantum principles are introduced, whose consolidation, as pillars of a graphical game-theoretical construction, is conducive to (i) the high-precision reproduction and reinterpretation of core experimental observations on neural coding in the self-organizing brain, with the instantaneous geometric dimensionality of neural…
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Taxonomy
TopicsFractal and DNA sequence analysis · Quantum Mechanics and Applications · Biofield Effects and Biophysics
