We present an account of neuroplasticity with respect to cell-internal processing pathways in relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the complexity of neuroplasticity. In these accounts, the model is a network of neurons connected by adaptive transmission links. The adaptation of the transmission links relies on weight changes according to use of the transmission link (short-term and long-term potentiation/depression). In contrast, we propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on its history of use) 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 network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by coupling strength. There is filtering and selection of signals for storage. Not every transmission event leaves a trace. 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：本文提出了关于神经可塑性的描述，重点关注细胞内部处理通路与膜可塑性及突触可塑性的关系。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充分捕捉神经可塑性的复杂性。在这些模型中，神经网络由通过自适应传输链路连接的神经元构成，而传输链路的自适应依赖于其使用频率导致的权重变化（如短时程和长时程增强/抑制）。相比之下，我们提出一种范式转换：从以突触为中心的模型（每个突触根据自身使用历史独立学习）转向以神经元为中心的模型（每个神经元利用信号选择来调控细胞内通路，从而实现膜层面的可塑性）。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）膜下区和细胞质中的内部参数，及其蛋白质信号网络；（c）细胞核中的核心参数，用于存储遗传和表观遗传信息。在以神经元为中心的模型中，网络中的每个节点（即神经元）拥有其独立的内部记忆。神经传导与信息存储相互分离，而非通过耦合强度自动关联。信号经过过滤和选择才被存储，并非每次传导事件都会留下痕迹。这代表了相对于突触权重模型的一个重要概念性进步。我们将神经元视为一种自编程装置，而非被动地由持续输入所决定。我们认为，新的神经建模方法是必要的，因为传统突触中心模型无法充分解释实验证据。最终，我们关注于一种灵活记忆系统的可能性，该系统根据其内在结构处理外部信号。