Khrennikov Andrei
International Center for Mathematical Modelling in Physics and Cognitive Sciences, Linnaeus University, Sweden.
Biosystems. 2011 Sep;105(3):250-62. doi: 10.1016/j.biosystems.2011.05.014. Epub 2011 Jun 12.
We propose a model of quantum-like (QL) processing of mental information. This model is based on quantum information theory. However, in contrast to models of "quantum physical brain" reducing mental activity (at least at the highest level) to quantum physical phenomena in the brain, our model matches well with the basic neuronal paradigm of the cognitive science. QL information processing is based (surprisingly) on classical electromagnetic signals induced by joint activity of neurons. This novel approach to quantum information is based on representation of quantum mechanics as a version of classical signal theory which was recently elaborated by the author. The brain uses the QL representation (QLR) for working with abstract concepts; concrete images are described by classical information theory. Two processes, classical and QL, are performed parallely. Moreover, information is actively transmitted from one representation to another. A QL concept given in our model by a density operator can generate a variety of concrete images given by temporal realizations of the corresponding (Gaussian) random signal. This signal has the covariance operator coinciding with the density operator encoding the abstract concept under consideration. The presence of various temporal scales in the brain plays the crucial role in creation of QLR in the brain. Moreover, in our model electromagnetic noise produced by neurons is a source of superstrong QL correlations between processes in different spatial domains in the brain; the binding problem is solved on the QL level, but with the aid of the classical background fluctuations.
我们提出了一种心智信息的类量子(QL)处理模型。该模型基于量子信息理论。然而,与将心智活动(至少在最高层面)简化为大脑中的量子物理现象的“量子物理大脑”模型不同,我们的模型与认知科学的基本神经元范式非常契合。QL信息处理(令人惊讶地)基于神经元联合活动所诱导的经典电磁信号。这种处理量子信息的新方法基于将量子力学表述为经典信号理论的一个版本,这是作者最近详细阐述的。大脑使用QL表示(QLR)来处理抽象概念;具体图像则由经典信息理论来描述。经典和QL这两个过程并行执行。此外,信息会在两种表示之间进行主动传递。我们模型中由密度算子给出的一个QL概念可以生成由相应(高斯)随机信号的时间实现所给出的各种具体图像。该信号的协方差算子与编码所考虑抽象概念的密度算子一致。大脑中各种时间尺度的存在在大脑中QLR的创建过程中起着关键作用。此外,在我们的模型中,神经元产生的电磁噪声是大脑中不同空间域内过程之间超强QL相关性的一个来源;绑定问题在QL层面上得到解决,但借助了经典背景波动。