In this position paper, we present a biological account of neuroplasticity with respect to cell-internal processing pathways and their relation to membrane and synaptic plasticity. We believe that traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the real complexity of neuroplasticity. In standard accounts, a neuronal network consists of a network of neurons connected by adaptive transmission links. Each neuron has a 'vertical' dimension where internal parameters steer the external membrane- and synapse-expressed parameters. In contrast to this, we propose a paradigm switch from a synapse-centric model to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by associative coupling strength. There is filtering and selection of signals for processing and storage. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure.

翻译：在本文中，我们从生物学角度阐述了神经可塑性，重点关注细胞内部处理通路及其与膜可塑性和突触可塑性的关系。我们认为，传统的以突触为中心、基于权重的记忆模型不足以也无法捕捉神经可塑性的真实复杂性。在标准描述中，神经网络由通过适应性传输链路连接的神经元网络组成。每个神经元具有一个“纵向”维度，其中内部参数控制着外部膜表达和突触表达的参数。与此相反，我们提出从以突触为中心的模型向以神经元为中心的模型进行范式转变（每个神经元通过信号选择细胞内通路，在膜上表达可塑性）。一个神经模型包括：（a）膜上参数的表达，特别是树突突触或棘突以及轴突末梢；（b）膜下区域和细胞质中的内部参数，及其蛋白质信号网络；（c）细胞核中用于遗传和表观遗传信息的核心参数。在以神经元为中心的模型中，水平网络中的每个节点（即神经元）都有其自身的内部记忆。神经传输和信息存储是分离的，而非通过关联耦合强度自动结合。信号在处理和存储前会经历过滤和选择。这代表了相对于突触权重模型的重要概念性进步。我们将神经元呈现为一个自编程装置，而非被动地由持续输入所决定。我们认为，由于传统以突触为中心的模型无法充分捕捉实验证据，因此有必要采用新的神经建模方法。最终，我们关注的是能够依据其内在结构处理外部信号的灵活记忆系统的可能性。