Learning by Growing: Dendritic Development Shapes Task Representations

In biological brains, form and computation are intrinsically linked: dendritic growth changes both which inputs a neuron receives and how it integrates them. Standard artificial neural networks represent connections as numerical weights without modeling their physical structure. This raises a question for brain-inspired AI: can task experience guide physical growth to build useful representations? Here we train simulated biophysical neuron populations through task-guided, geometrically constrained dendritic growth and pruning. Synaptic strengths follow geometry rather than independent optimization, and a separately fitted linear readout predicts from population responses. Across synchrony and displacement tasks, growth builds predictive representations. Crucially, reassigning the same selected inputs among neurons reduces performance even when learned trees and contact strengths are preserved and the readout is retrained. This allocation advantage develops during learning. Learned populations also benefit more from combining neuronal responses than remapped populations do, supporting the value of complementary population features. More connections do not always help: in synchrony detection, active dendritic performance declines modestly at higher connectivity while a point-neuron baseline continues to improve. These findings connect biological development to representation learning, showing how task-guided growth establishes useful input organization beyond source selection. Ultimately, they reveal how intelligent representations are fundamentally rooted in their underlying physical morphology, providing a framework for investigating the formation of structural inductive biases.