The quantum brain

眞里 治部, 邦夫 保江

1. Prologue 2. Acknowledgments 3. Part I: A Tutorial in Quantum Physics 4. 1. Quanta and Fields 5. 2. Quantum Mechanics 6. 3. Quantum Field Theory 7. Part II: Theories of Brain Functioning 8. 4. Classical Theory of the Brain 9. 5. Phusiological Theory of the Brain 10. 6. Dynamical Theory of the Brain 11. Part III: A Quantum Approach to Brain Functioning, Consciousness and Life 12. 7. Quantum Theory of the Brain 13. 8. Quantum Brain Dynamics 14. 9. Quantum Theory of Consciousness 15. Epilogue 16. References 17. Appendices 18. Index

RODNEY M. J. COTTERILL

Jeffrey Satinover

Hoang-Quan Nguyen, Xuan-Bac Nguyen, Hugh Churchill +4 more

Vision-brain understanding aims to extract semantic information about brain signals from human perceptions. Existing deep learning methods for vision-brain understanding are usually introduced in a traditional learning paradigm missing the ability to learn the connectivities between brain regions. Meanwhile, the quantum computing theory offers a new paradigm for designing deep learning models. Motivated by the connectivities in the brain signals and the entanglement properties in quantum computing, we propose a novel Quantum-Brain approach, a quantum-inspired neural network, to tackle the vision-brain understanding problem. To compute the connectivity between areas in brain signals, we introduce a new Quantum-Inspired Voxel-Controlling module to learn the impact of a brain voxel on others represented in the Hilbert space. To effectively learn connectivity, a novel Phase-Shifting module is presented to calibrate the value of the brain signals. Finally, we introduce a new Measurement-like Projection module to present the connectivity information from the Hilbert space into the feature space. The proposed approach can learn to find the connectivities between fMRI voxels and enhance the semantic information obtained from human perceptions. Our experimental results on the Natural Scene Dataset benchmarks illustrate the effectiveness of the proposed method with Top-1 accuracies of 95.1% and 95.6% on image and brain retrieval tasks and an Inception score of 95.3% on fMRI-to-image reconstruction task. Our proposed quantum-inspired network brings a potential paradigm to solving the vision-brain problems via the quantum computing theory.

Dozie Iwuh

What Quantum Brain Dynamics (QBD) considers is not just these other functions of the brain, this is because they can be well analyzed with the workings of classical mechanics (even though they still play host to a quantum description). It rather considers two specific functions above all else consciousness and memory. QBD falls in line umbrella-covers aspects of the quantum brain analysis such as quantum-consciousness, quantum-mind and quantum-brain. The inspiration that lurks behind the Quantum Interpretation of the Brain (QIB), is traceable to the 1944 article written by E. Schrodinger, What is Life, in which he presents how a living organisms evades decay to equilibrium by the fact of negentropy, as such life which is in its ordered macroscopic state is created (in an environment of disorder), which moves against the second law of thermodynamics. The life that is created, that which is sustained, arises from an interaction that the organism engages in with the environment. This interaction is microscopic, albeit quantum, it is an interaction that underscores the reality of quantum entanglement (which also plays hosts to the superposition of quantum states). The quantum interpretation of the brain is a nascent, yet burgeoning as it might be that necessary tool required for a better articulation and comprehension of the brain.

Andrei Yu. Khrennikov