There is mounting experimental evidence that brain-state specific neural mechanisms supported by connectomic architectures serve to combine past and contextual knowledge with current, incoming flow of evidence (e.g. from sensory systems). Such mechanisms are distributed across multiple spatial and temporal scales and require dedicated support at the levels of individual neurons and synapses. A prominent feature in the neocortex is the structure of large, deep pyramidal neurons which show a peculiar separation between an apical dendritic compartment and a basal dentritic/peri-somatic compartment, with distinctive patterns of incoming connections and brain-state specific activation mechanisms, namely apical-amplification, -isolation and -drive associated to the wakefulness, deeper NREM sleep stages and REM sleep. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning spiking networks are based on single compartment neurons that miss the description of mechanisms to combine apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model which includes features that are essential for supporting brain-state specific learning and with a piece-wise linear transfer function (ThetaPlanes) at highest abstraction level to be used in large scale bio-inspired artificial intelligence systems. A machine learning algorithm, constrained by a set of fitness functions, selected the parameters defining 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，能够将过去和情境知识与当前传入的证据流（例如来自感觉系统）相结合。这些机制分布在多个空间和时间尺度上，并在单个神经元和突触层面需要专门的支持。新皮层的一个显著特征是大型深层锥体神经元的结构，这些神经元表现出顶端树突房室与基底树突/胞体周围房室之间的特殊分离，具有独特的传入连接模式和脑状态特异性激活机制，即与觉醒、深度非快速眼动睡眠阶段及快速眼动睡眠相关的顶端放大、隔离和驱动机制。顶端机制的认知作用已在行为动物实验中得以证实。相比之下，经典的学习型脉冲网络模型基于单房室神经元，缺乏结合顶端与基底/胞体信息的机制描述。本研究旨在为计算领域提供一个双房室脉冲神经元模型，该模型包含支持脑状态特异性学习的关键特征，并采用最高抽象层次的分段线性传递函数（ThetaPlanes），适用于大规模仿生人工智能系统。一种受一组适应度函数约束的机器学习算法，选择了定义表达所需顶端机制神经元的参数。