Topic: somatodendritic coupling

To resolve the fundamental question of how the mammalian neocortex learns to create complex hierarchical representations, two main competing theories that intersect ideas from neuroscience and machine learning are currently debated. The first theory draws inspiration from artificial neural networks, suggesting the brain approximates error backpropagation (BP), where neurons adjust synapses to minimize a backpropagated error. The second approach, target learning (TL), proposes that neurons learn by reducing feedback needed to achieve desired activity patterns. While both theories are computati