Shin Soonho, Oh Joonsu, Kim Sun Kwang, Lee Yong-Seok, Kim Sang Jeong
Department of Physiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
Exp Mol Med. 2025 May 7. doi: 10.1038/s12276-025-01456-7.
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.
不确定性在感觉处理中至关重要，这使得有必要对其在感觉皮层中的神经表征进行进一步的定量研究。在此，为满足这一需求，我们采用深度学习方法来量化在振动频率辨别任务期间，来自初级体感皮层前肢区域（fS1）的神经活动中的不确定性，引入了一个旨在解码随时间不一致跟踪的神经数据的Transformer模型。我们的模型表明，fS1的神经表征不仅编码来自振动刺激的不确定性，还编码来自决策过程的不确定性，强调了其在各种生物学背景中的关键作用。我们证实，随着学习的进行，不确定性会降低，而随着学习中断会增加。与先前研究的预测一致，我们还观察到在心理测量阈值处不确定性较高。此外，高不确定性与错误决策相关，并且我们已经确定了先前试验和当前试验之间不确定性的动态变化。这些发现强调了随着学习的进行，fS1在为大脑下游区域评估不确定性方面不断演变的作用。
