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Nature communications
03 25, 2020;11 (1): 1554.

Somatodendritic consistency check for temporal feature segmentation.

Toshitake Asabuki, Tomoki Fukai
Author Information
  1. Toshitake Asabuki: Department of Complexity Science and Engineering, University of Tokyo, Kashiwa, Chiba, 277-8561, Japan.
  2. Tomoki Fukai: Department of Complexity Science and Engineering, University of Tokyo, Kashiwa, Chiba, 277-8561, Japan. tomoki.fukai@oist.jp.
PMID: 32214100 DOI: 10.1038/s41467-020-15367-w

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.

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MeSH Term

Action Potentials
Brain
Computational Biology
Dendrites
Learning
Membrane Potentials
Models, Neurological
Nerve Net
Neuronal Plasticity
Neurons
Journal Article Research Support, Non-U.S. Gov't
Article has an altmetric score of 2

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Created with Highcharts 10.0.0temporalpotentiallyfeaturesinformationstreamsprocessdendritesneuronsmodelsomaticnetworksfeatureneuralbrainidentifiessalientwithincontinuoushierarchicaleventsrequirescompressioneffectivecomputationalprinciplesyetexploredBackpropagatingactionpotentialscaninducesynapticplasticitycorticalpyramidalanalogyeffectself-supervisingincreasessimilaritydendriticactivitiesactivitynormalizedrunningaverageshowfamilycomposedtwo-compartmentperformssurprisinglywidevarietycomplexunsupervisedlearningtasksincludingchunkingsequencessourceseparationmixedcorrelatedsignalsCommonmethodsapplicableanalysespreviouslyunknownresultssuggestpowerfulabilityanalyzesimpleneuronmayalsousefulengineeringapplicationsSomatodendriticconsistencychecksegmentation

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