In this paper, we lay out a novel model of neuroplasticity in the form of a horizontal-vertical integration model of neural processing. We believe a new approach to neural modeling will benefit the 3rd wave of AI. The horizontal plane consists of an adaptive network of neurons connected by transmission links which generates spatio-temporal spike patterns. This fits with standard computational neuroscience approaches. Additionally for each individual neuron there is a vertical part consisting of internal adaptive parameters steering the external membrane-expressed parameters which are involved in neural transmission. Each neuron has a vertical modular system of parameters corresponding to (a) external parameters at the membrane layer, divided into compartments (spines, boutons) (b) internal parameters in the submembrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In such models, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are systematically separated, an important conceptual advance over synaptic weight models. We discuss the membrane-based (external) filtering and selection of outside signals for processing vs. signal loss by fast fluctuations and the neuron-internal computing strategies from intracellular protein signaling to the nucleus as the core system. We want to show that the individual neuron has an important role in the computation of signals and that many assumptions derived from the synaptic weight adjustment hypothesis of memory may not hold in a real brain. Not every transmission event leaves a trace and the neuron is a self-programming device, rather than passively determined by current input. Ultimately we strive to build a flexible memory system that processes facts and events automatically.

中文翻译：

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神经模型的新方法草图

在本文中，我们以神经处理的水平-垂直整合模型的形式提出了一种新的神经可塑性模型。我们相信一种新的神经建模方法将使第三波人工智能受益。水平面由一个由传输链路连接的自适应神经元网络组成，该网络生成时空尖峰模式。这符合标准的计算神经科学方法。此外，对于每个单独的神经元，都有一个由内部自适应参数组成的垂直部分，这些参数引导参与神经传递的外部膜表达参数。每个神经元都有一个垂直模块化的参数系统，对应于（a）膜层的外部参数，分为隔室（脊椎，boutons）（b）膜下区和细胞质的内部参数及其蛋白质信号网络和（c）细胞核中遗传和表观遗传信息的核心参数。在这样的模型中，水平网络中的每个节点（=神经元）都有自己的内部存储器。神经传输和信息存储被系统地分离，这是突触权重模型的重要概念进步。我们讨论了基于膜的（外部）过滤和选择用于处理的外部信号与快速波动导致的信号损失以及从细胞内蛋白质信号传导到作为核心系统的细胞核的神经元内部计算策略。我们想证明单个神经元在信号计算中具有重要作用，并且从记忆的突触权重调整假设得出的许多假设在真实的大脑中可能并不成立。并非每个传输事件都会留下痕迹，并且神经元是一个自编程设备，而不是被动地由电流输入决定。最终，我们努力构建一个灵活的记忆系统，自动处理事实和事件。