Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and brain-state-specific activation mechanisms, namely, apical amplification, isolation, and drive, which are associated with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model that incorporates features essential for supporting brain-state-specific learning. This model includes a piece-wise linear transfer function (ThetaPlanes) at the highest abstraction level, making it suitable for use in large-scale bio-inspired artificial intelligence systems. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected the parameters that define neurons expressing the desired apical mechanisms.

翻译：越来越多的实验证据表明，由连接组架构支持的脑状态特异性神经机制，在整合过往及情境知识与当前传入证据（例如来自感觉系统）的持续流中发挥着关键作用。这些机制跨越多个空间和时间尺度运作，需要在单个神经元和突触层面提供专门支持。新皮质内的一个显著特征是大型深层锥体神经元的独特结构，其顶端树突房室与基底树突/胞体周围房室之间存在显著分离。这种分离以传入连接的不同模式以及脑状态特异性激活机制（即顶端放大、隔离和驱动）为特征，这些机制分别与清醒状态、深度非快速眼动睡眠期和快速眼动睡眠相关。顶端机制的认知作用已在行为动物实验中得到证实。相比之下，基于单房室神经元的经典尖峰网络学习模型无法描述顶端与基底/胞体信息的整合过程。本研究旨在为计算科学界提供一个双房室尖峰神经元模型，该模型纳入了支持脑状态特异性学习的关键特征。此模型在最高抽象层级采用分段线性传递函数（ThetaPlanes），使其适用于大规模生物启发式人工智能系统。通过一组适应度函数引导的机器学习进化算法，筛选出能够表达所需顶端机制神经元的参数定义。