Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment. We show that this learning process, termed coincidence detection, is robust against strong distractions in the basal input space and demonstrate its effectiveness in a linear classification task.

中文翻译：

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用于监督学习的非线性树突符合检测

皮质锥体神经元具有复杂的树突解剖结构，其功能是一个活跃的研究领域。特别是，它的胞体和顶端树突树之间的分离被认为在处理前馈感觉信息和自上而下或反馈信号中起着积极的作用。在这项工作中，我们使用一个简单的两室模型来解释基底和顶端输入流之间的非线性相互作用，并表明基底室中的标准无监督 Hebbian 学习规则允许神经元将前馈基底输入与顶部对齐。心尖室接收到的下靶信号。我们表明，这种称为巧合检测的学习过程对基础输入空间中的强烈干扰具有鲁棒性，并证明了其在线性分类任务中的有效性。