Familiarity training enhance straightening of neural trajectory for video prediction

Abstract

Predictive processing in the visual system is pivotal for efficient sensory-driven behaviors. Previous research has shown that the visual system transforms sequential inputs into straighter temporal trajectories. However, the specific role of neural straightening in predictive processing, especially in how learning reshapes this phenomenon for enhanced prediction, remains unclear. To address this, we analyzed V2 population activity in macaques during familiarity training with video stimuli. Our findings reveal that repeated exposure to the same movies significantly enhances neural straightening, indicating a critical role of learning in refining neural trajectories for prediction. In parallel, our studies with the deep predictive network model, Pred-Net, demonstrated similar enhancements in neural straightening in response to familiar movies. This underscores a strong association between neural straightening and predictive coding. Together, our results provide novel insights into the adaptive mechanisms of the visual cortex, enriching our understanding of how learning shapes neural path-ways for efficient prediction.