Quantitative dynamics of neural uncertainty in sensory processing and decision-making during discriminative learning

Uncertainty is crucial in sensory processing, necessitating further quantitative research on its neural representation in the sensory cortex. Here, to address this need, we used a deep learning approach to quantify uncertainties in neural activity from the forelimb area of the primary somatosensory cortex (fS1) during a vibration frequency discrimination task, introducing a transformer model designed to decode neural data not consistently tracked over time. Our model shows that the neural representation of fS1 encodes uncertainties not only from vibratory stimuli but also from decision-making processes, emphasizing its crucial role across various biological contexts. We confirmed that uncertainty decreases as learning progresses and increases with interruptions in learning. In line with predictions from previous studies, we also observed that uncertainty is high at psychometric thresholds. Furthermore, high uncertainty correlates with incorrect decisions, and we have identified dynamics in uncertainty between previous and current trials. Such findings underscore the evolving role of fS1 in assessing uncertainty for the brain's downstream areas as learning progresses.