标题： 回复关于《神经电动力学：真正的概念性陷阱在哪里？》的评论 显示英文标题

标题： Reply to Comments on Neuroelectrodynamics: Where are the Real Conceptual Pitfalls?

摘要： 大脑中计算的基本强大过程被广泛误解。 论文[1]将目前无法构建智能思维机器的普遍失败归因于当前的时间编码还原论原则，并主张在大脑类比方面改变范式。 由于信息片段存储在蛋白质中，这些蛋白质可以在几种结构之间转换以执行其功能，因此生物底物积极参与物理计算。 动作电位和突触活动的内在非线性动力学维持了大脑中神经元内部和之间的物理相互作用。 在这些事件中，存储信息片段的分子结构（蛋白质）与产生并整合信息的电流之间交换所需的信息。 整个物理相互作用过程解释了大脑如何主动创造或体验意义。 在动作电位生成期间的相互作用过程可以简单地看作是神经元解决多体问题的时刻。 一种神经电磁理论表明，神经元解决方程，而不仅仅是单纯计算函数。 以时间模式为主要焦点，尖峰时间教条（STD）忽视了发生在神经元内部的重要计算形式。 此外，人工神经模型忽略了最重要的一部分，因为大脑真正的超级计算能力源于发生在神经元内部的计算。 显示英文摘要

摘要： The fundamental, powerful process of computation in the brain has been widely misunderstood. The paper [1] associates the general failure to build intelligent thinking machines with current reductionist principles of temporal coding and advocates for a change in paradigm regarding the brain analogy. Since fragments of information are stored in proteins which can shift between several structures to perform their function, the biological substrate is actively involved in physical computation. The intrinsic nonlinear dynamics of action potentials and synaptic activities maintain physical interactions within and between neurons in the brain. During these events the required information is exchanged between molecular structures (proteins) which store fragments of information and the generated electric flux which carries and integrates information in the brain. The entire process of physical interaction explains how the brain actively creates or experiences meaning. This process of interaction during an action potential generation can be simply seen as the moment when the neuron solves a many-body problem. A neuroelectrodynamic theory shows that the neuron solves equations rather than exclusively computes functions. With the main focus on temporal patterns, the spike timing dogma (STD) has neglected important forms of computation which do occur inside neurons. In addition, artificial neural models have missed the most important part since the real super-computing power of the brain has its origins in computations that occur within neurons.