Orientation selective dendritic feature representation supports robust pattern identification

Orientation-selective synaptic inputs in visual cortex motivate studying how feature selectivity is formed and represented within dendritic computations, and how dendritic representations influence network-level visual processing. We investigate this question using a Two-Stage Dendritic Network (TSDN), in which multiple dendritic branches process local inputs and their nonlinear responses are integrated at the soma. Hebbian and anti-Hebbian plasticity first learn feature detectors from unlabeled inputs; these detectors are then frozen while somatic integration and a classification readout are trained with supervision. Gabor analysis reveals orientation-selective feature structure, while ablations show that anti-Hebbian plasticity reduces detector redundancy and improves downstream classification relative to Hebbian-only learning. Across five image datasets, TSDN achieves higher mean classification accuracy than the tested backpropagation-trained baselines. It also exhibits improved robustness to Gaussian input noise on all tested datasets except CIFAR-10. Varying the anti-Hebbian learning rate reveals a discriminability–robustness trade-off, accompanied by non-monotonic changes in feature frequency content and branch-response sensitivity. These findings connect unsupervised dendritic feature formation with recognition and noise tolerance, highlighting plasticity strength as a factor shaping the computational properties of dendritic representations.