The brain identifies potentially salient features within continuous information streams to process hierarchical temporal events. This requires the compression of information streams, for which effective computational principles are yet to be explored. Backpropagating action potentials can induce synaptic plasticity in the dendrites of cortical pyramidal neurons. By analogy with this effect, we model a self-supervising process that increases the similarity between dendritic and somatic activities where the somatic activity is normalized by a running average. We further show that a family of networks composed of the two-compartment neurons performs a surprisingly wide variety of complex unsupervised learning tasks, including chunking of temporal sequences and the source separation of mixed correlated signals. Common methods applicable to these temporal feature analyses were previously unknown. Our results suggest the powerful ability of neural networks with dendrites to analyze temporal features. This simple neuron model may also be potentially useful in neural engineering applications.

大脑识别连续信息流中潜在的显着特征，以处理分层的时间事件。这需要信息流的压缩，对此尚需探索有效的计算原理。反向传播的动作电位可以在皮质锥体神经元的树突中诱导突触可塑性。以此效果为类比，我们对自我监督过程进行建模，该过程可提高树突状体活动与体细胞活动之间的相似性，其中体细胞活动通过移动平均值进行归一化。我们进一步表明，由两室神经元组成的网络家族执行了令人惊讶的各种复杂的无监督学习任务，包括时间序列的分块和混合相关信号的源分离。适用于这些时间特征分析的常用方法以前是未知的。我们的结果表明带有树突的神经网络分析时间特征的强大能力。这个简单的神经元模型在神经工程应用中也可能潜在有用。