ArticleNature communications2020
Somatodendritic consistency check for temporal feature segmentation.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Transient boosting of action potential backpropagation for few-shot temporal pattern learning.PLoS computational biology · 2025Article
- Learning predictive signals within a local recurrent circuit.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Predictive learning rules generate a cortical-like replay of probabilistic sensory experiences.eLife · 2025Article
- Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity.eLife · 2025Article
- Article
- Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.PLoS computational biology · 2024Article
- Neural circuit mechanisms of hierarchical sequence learning tested on large-scale recording data.PLoS computational biology · 2022Article
- Sequence learning, prediction, and replay in networks of spiking neurons.PLoS computational biology · 2022Article
- Modeling the Repetition-Based Recovering of Acoustic and Visual Sources With Dendritic Neurons.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.