近日，瑞士洛桑联邦理工学院Alexander Mathis及其研究小组发现，任务驱动的神经网络模型可预测本体感觉的神经动态。相关论文于2024年3月21日在线发表于国际学术期刊《细胞》。

研究人员采用任务驱动建模方法，研究了楔束核（CN）和体感皮层第2区（S1）本体感觉神经元的神经密码。研究人员通过肌肉骨骼建模模拟了肌肉主轴信号，并生成了大规模运动剧目，以训练基于16个假设的神经网络，每个假设代表不同的计算目标。

研究人员发现，新出现的、经过任务优化的内部表征能从合成数据中，概括出灵长类动物CN和S1的神经动态预测。旨在预测肢体位置和速度的计算任务最能预测这两个区域的神经活动。

由于任务优化开发出的表征能更好地预测主动运动时的神经活动，而不是被动运动时的神经活动，因此研究人员推测，在目标定向运动时，中枢神经和中枢神经活动是自上而下调节的。

据悉，本体感觉根据分布式感觉神经元告诉大脑身体的状态。然而，人们对本体感觉处理的原理知之甚少。

附：英文原文

Title: Task-driven neural network models predict neural dynamics of proprioception

Author: Alessandro Marin Vargas, Axel Bisi, Alberto S. Chiappa, Chris Versteeg, Lee E. Miller, Alexander Mathis

Issue&Volume: 2024-03-21

Abstract: Proprioception tells the brain the state of the body based on distributed sensory neurons. Yet, the principles that govern proprioceptive processing are poorly understood. Here, we employ a task-driven modeling approach to investigate the neural code of proprioceptive neurons in cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal modeling and generated a large-scale movement repertoire to train neural networks based on 16 hypotheses, each representing different computational goals. We found that the emerging, task-optimized internal representations generalize from synthetic data to predict neural dynamics in CN and S1 of primates. Computational tasks that aim to predict the limb position and velocity were the best at predicting the neural activity in both areas. Since task optimization develops representations that better predict neural activity during active than passive movements, we postulate that neural activity in the CN and S1 is top-down modulated during goal-directed movements.

DOI: 10.1016/j.cell.2024.02.036

Source: https://www.cell.com/cell/fulltext/S0092-8674(24)00239-3