任务学习增加猕猴视觉皮层神经反应的信息冗余

Shizhao Liu, Anton Pletenev, Ralf M. Haefner*†, Adam C. Snyder† INTRODUCTION: How does the brain transform sensory input into perception and behavior? The classic model guiding most of neuroscience and modern deep learning views perception as a largely feedforward process: Sensory signals are transformed from early to higher visual areas to make behaviorally relevant information more explicit. Feedback connections are thought to merely fine-tune this process—enhancing relevant features or suppressing noise during attention and learning. RESULTS: At the start of learning, redundancy was near zero, indicating largely independent neural responses. Over the course of training, redundancy increased, ultimately reaching levels where roughly half of each neuron’s information was shared with other recorded neurons. …