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Neural Network for Quantum Brain Dynamics: 4D CP~1+U(1) Gauge Theory on Lattice and Its Phase Structure

机译:量子大脑动力学的神经网络:晶格及其相结构的4D CP〜1 + U(1)量规理论

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摘要

We consider a system of two-level quantum quasi-spins and gauge bosons put on a 3+1D lattice. As a model of neural network of the brain functions, these spins describe neurons quantum-mechanically, and the gauge bosons describes weights of synaptic connections. It is a generalization of the Hopfield model to a quantum network with dynamical synaptic weights. At the microscopic level, this system becomes a model of quantum brain dynamics proposed by Umezawa et al., where spins and gauge field describe water molecules and photons, respectively. We calculate the phase diagram of this system under quantum and thermal fluctuations, and find that there are three phases; confinement, Coulomb, and Higgs phases. Each phase is classified according to the ability to learn patterns and recall them. By comparing the phase diagram with that of classical networks, we discuss the effect of quantum fluctuations and thermal fluctuations (noises in signal propagations) on the brain functions.
机译:我们考虑放置在3 + 1D晶格上的两级量子准自旋和规范玻色子的系统。作为大脑功能神经网络的模型,这些自旋以量子力学方式描述神经元,而规范玻色子则描述突触连接的权重。它是Hopfield模型对具有动态突触权重的量子网络的一般化。在微观层面上,该系统成为Umezawa等人提出的量子脑动力学模型,其中自旋和规范场分别描述了水分子和光子。我们计算了该系统在量子和热涨落下的相位图,发现存在三个相位。限制,库仑和希格斯阶段。每个阶段都根据学习模式和调用模式的能力进行分类。通过将相图与经典网络的相图进行比较,我们讨论了量子涨落和热涨落(信号传播中的噪声)对大脑功能的影响。

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