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 apical tree believed to play role in processing feed-forward sensory information top-down or feedback signals. this work, we use simple two-compartment model accounting for nonlinear interactions basal input streams show that standard unsupervised Hebbian learning rules compartment allow neuron align with target signal received by compartment. We process, termed coincidence detection, robust against strong distractions space demonstrate effectiveness linear classification task.