Nonlinear dendritic coincidence detection for supervised learning

  • Cortical pyramidal neurons have a complex dendritic anatomy, whose function is an active research field. In particular, the segregation between its soma and the apical dendritic tree is believed to play an active role in processing feed-forward sensory information and top-down or feedback signals. In this work, we use a simple two-compartment model accounting for the nonlinear interactions between basal and apical input streams and show that standard unsupervised Hebbian learning rules in the basal compartment allow the neuron to align the feed-forward basal input with the top-down target signal received by the apical compartment. We show that this learning process, termed coincidence detection, is robust against strong distractions in the basal input space and demonstrate its effectiveness in a linear classification task.
Author:Fabian SchubertORCiDGND, Claudius GrosORCiDGND
Parent Title (English):Frontiers in computational neuroscience
Publisher:Frontiers Research Foundation
Place of publication:Lausanne
Document Type:Article
Date of Publication (online):2021/08/04
Date of first Publication:2021/08/04
Publishing Institution:Universit├Ątsbibliothek Johann Christian Senckenberg
Release Date:2021/08/30
Tag:coincidence detection; dendrites; plasticity; pyramidal neuron; supervised learning
Issue:art. 718020
Page Number:9
First Page:1
Last Page:9
We acknowledge the financial support of the German Research Foundation (DFG).
Institutes:Physik / Physik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
3 Sozialwissenschaften / 37 Bildung und Erziehung / 370 Bildung und Erziehung
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0