标题： 层次调制脉冲神经网络中抽象规则出现的神经元和结构分化 显示英文标题

标题： Neuronal and structural differentiation in the emergence of abstract rules in hierarchically modulated spiking neural networks

摘要： 从实例中出现抽象规则是大脑灵活泛化和快速适应能力的核心。 然而，规则抽象背后的内部组织机制仍然不明确，这主要是由于传统模型缺乏内在神经元异质性，使得难以研究神经元和结构的分化。 受星形胶质细胞介导的神经调节启发，这项工作引入了一个分层调制的循环脉冲神经网络（HM-RSNN），它可以调整内在神经元特性，其中全局阶段模拟钙波驱动的任务特定配置，局部阶段模仿胶质递质介导的微调。 我们在四个认知任务中使用HM-RSNN进行建模，在网络和神经元层面都观察到了规则抽象相关的分化，与人工神经网络相比，性能更好。 这些发现突显了动态内部组织在支持完成各种认知任务中的关键作用。 显示英文摘要

摘要： The emergence of abstract rules from exemplars is central to the brain's capability of flexible generalization and rapid adaptation. However, the internal organizing mechanisms underlying rule abstraction remain elusive, largely due to the limitations of conventional models that lack intrinsic neuronal heterogeneity, making it hard to examine neuronal and structural differentiations. Inspired by astrocyte-mediated neuromodulation, this work introduces a hierarchically modulated recurrent spiking neural network (HM-RSNN) that can tune intrinsic neuronal properties, where a global stage simulates calcium wave-driven task-specific configuration and a local one mimics gliotransmitter-mediated fine-tuning. We conduct modeling using HM-RSNN across four cognitive tasks and rule abstraction contingent differentiation is observed at both network and neuron levels, leading to better performance compared to artificial neural networks. These findings highlight the critical role of dynamic internal organization in supporting the accomplishment of various cognitive tasks.