Local learning needs dendrites

Riccardo Zecchina presents a learning rule for recurrent neural networks in which nonlinear dendrites create and stabilize task-relevant attractors within a representation manifold. Learning is local in both space and time. On the same network architecture, the scheme matches the performance of backpropagation through time while taking a fundamentally different path through weight space. Statistical-physics analysis explains how dendritic gating and the geometry of the representation manifold support this alternative mechanism for learning.