In this position paper, we present biological detail about 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. The adaptation of these transmission links is overly simplified in the standard model of short-term and long-term potentiation or depression assuming weight adaptation according to use. We propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on associative coupling) to a neuron-centric model (each neuron uses its intracellular pathways to express plasticity at its synapses and dendritic membrane). Each neuron has a 'vertical' dimension where internal parameters steer the external membrane- and synapse-expressed parameters. 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 neuron in the horizontal network has its own internal memory. Transmission and memory are separate, not linked by strict use-dependence. There is filtering and selection of signals for processing and storage. Not every transmission event leaves a trace. This is a conceptual advance over synaptic weight models. The neuron is a self-programming device, rather than a transfer function determined by input. A new approach to neural modeling is better able to capture experimental evidence than synapse-centric models.

翻译：在这篇立场论文中，我们提出了关于神经可塑性的生物学细节，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充分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。这些传输链路的适应性在短期和长期增强或抑制的标准模型中被过度简化，仅假设权重根据使用情况而调整。我们提出从以突触为中心的模型（每个突触基于关联耦合独立学习）转向以神经元为中心的模型（每个神经元利用其细胞内通路在其突触和树突膜上表达可塑性）。每个神经元都具有一个"垂直"维度，其中内部参数调控外部膜及突触表达的参数。神经模型包含：（a）膜上参数表达，特别是树突突触或棘突，以及轴突终扣；（b）亚膜区与细胞质中的内部参数及其蛋白质信号网络；（c）细胞核中存储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，水平网络中的每个神经元都拥有其内部记忆。传输与记忆是分离的，并非通过严格的使用依赖性相连接。信号在加工和存储前会经过筛选与选择。并非每次传输事件都会留下痕迹。这相较于突触权重模型是一次概念上的进步。神经元是一种自我编程装置，而非由输入决定的传递函数。这种神经建模的新方法比以突触为中心的模型更能有效捕捉实验证据。