Topic: lateral visual cortex

How do we learn about experiences while being removed from the experience itself? One solution involves offline learning via reactivations. Sensory reactivation involves the recurrence of a pattern of neural activity during sleep or quiet waking that previously occurred during a sensory experience. Prevailing theories of offline memory consolidation posit that the pattern of neurons activated during a sensory experience will be faithfully reactivated, thereby stabilizing the entire pattern. However, sensory-evoked patterns are not stable, but instead drift across repeated experiences. Our stud

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