Nonlinear Dendritic Coincidence Detection for Supervised Learning

TL;DR

This paper introduces a two-compartment neuron model demonstrating that nonlinear dendritic interactions enable coincidence detection, aligning feed-forward inputs with top-down signals for robust supervised learning.

Contribution

It presents a novel two-compartment neuron model showing how nonlinear dendritic interactions facilitate supervised learning through coincidence detection.

Findings

Robust alignment of basal and apical inputs despite distractions

Effective linear classification using the proposed model

Hebbian learning rules enable coincidence detection in neurons

Abstract

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.