Abstract:
: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.
journal_name
Nat Communjournal_title
Nature communicationsauthors
Asabuki T,Fukai Tdoi
10.1038/s41467-020-15367-wsubject
Has Abstractpub_date
2020-03-25 00:00:00pages
1554issue
1issn
2041-1723pii
10.1038/s41467-020-15367-wjournal_volume
11pub_type
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