TY  - JOUR
A1  - Schubert, Fabian
A1  - Gros, Claudius
T1  - Nonlinear dendritic coincidence detection for supervised learning
T2  - Frontiers in computational neuroscience
N2  - 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.
KW  - dendrites
KW  - pyramidal neuron
KW  - plasticity
KW  - coincidence detection
KW  - supervised learning
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/62430
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-624308
SN  - 1662-5188
N1  - We acknowledge the financial support of the German Research Foundation (DFG).
VL  - 15
IS  - art. 718020
SP  - 1
EP  - 9
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -