Matter-antimatter asymmetry restrains the dimensionality of neural representations: quantum decryption of large-scale neural coding
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 traditional 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, inter-relativistic quantum principles are introduced, whose consolidation, as spin-geometrical pillars of a network- and 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 representations of a spontaneous behavioral state being proven to be at most 16, unidirectionally; (ii) a possible role for spinor (co-)representations, as the latent building blocks of self-organizing cortical circuits subserving (co-)behavioral states; (iii) an early crystallization of pertinent multidimensional synaptic (co-)architectures, whereby Lorentz (co-)partitions are in principle verifiable; and, ultimately, (iv) potentially inverse insights into matter-antimatter asymmetry. New avenues for the decryption of large-scale neural coding in health and disease are being discussed.
Code (0)
등록된 구현이 없습니다.
Tasks
DisentanglementSimilar Papers 제목 키워드 기반
Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors
Matter-antimatter asymmetry is one of the major unsolved problems in physics that can be probed through precision measurements of charge-parity symmetry violation at current and next-generation neutrino oscillation exper…
Synthetic Data GenerationEfficient Antihydrogen Detection in Antimatter Physics by Deep Learning
Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry and antigravity effects require the efficient detection of antihydrogen …
Deep LearningGenerative Models for Simulation of KamLAND-Zen
The next generation of searches for neutrinoless double beta decay (0{\nu}\b{eta}\b{eta}) are poised to answer deep questions on the nature of neutrinos and the source of the Universe's matter-antimatter asymmetry. They …
AI Meets Antimatter: Unveiling Antihydrogen Annihilations
The ALPHA-g experiment at CERN aims to perform the first-ever direct measurement of the effect of gravity on antimatter, determining its weight to within 1% precision. This measurement requires an accurate prediction of …
Deep LearningPositionSelf-Supervised Visual On-Policy Distillation
Visual on-policy distillation relies heavily on an informative teacher-student asymmetry, through either a larger, stronger teacher or privileged supervision, such as reference answers or ground-truth regions of interest…