Artificial Dendritic Neurons Enable Self-Supervised Temporal Feature Extraction

The brain identifies potentially salient features within continuous information streams to appropriately process external and internal temporal events. This requires the compression or abstraction of information streams, for which no effective information principles are known. Here, we propose conditional entropy minimization learning as the fundamental principle of such temporal processing. We show that this learning rule resembles Hebbian learning with backpropagating action potentials in dendritic neuron models. Moreover, networks of the dendritic neurons can perform a surprisingly wide variety of complex unsupervised learning tasks. Our model not only accounts for the mechanisms of chunking of temporal inputs in the human brain but also accomplishes blind source separation of correlated mixed signals, which cannot be solved by conventional machine learning methods, such as independent-component analysis. One Sentence Summary Neurons use soma-dendrite interactions to self-supervise the learning of characteristic features of various temporal inputs.