Chuchu Liu

Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Bioscience Methods, 2024, Vol. 15, No. 1   doi: 10.5376/bm.2024.15.0003
Received: 01 Jan., 2024    Accepted: 03 Feb., 2024    Published: 14 Feb., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Liu N., 2024, Unveiling the mechanism of proprioception in primates: the application of task-driven neural network models, Bioscience Method, 15(1): 21-27 (doi: 10.5376/bm.2024.15.0003)

The paper titled "Task-driven neural network models predict neural dynamics of proprioception" was published in the journal Cell on March 21, 2024, by authors Alessandro Marin Vargas, Axel Bisi, Alberto S. Chiappa, Chris Versteeg, Lee E. Miller, and Alexander Mathis, are from the 1Brain Mind Institute, School of Life Sciences, Ecole Polytechnique Fe´ de´rale de Lausanne (EPFL), 1015 Lausanne, Switzerland; Department of Neuroscience, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA, and others. This study delved into the neural dynamics of proprioception in primates under active and passive movement conditions through the establishment of task-driven neural network models. Utilizing synthetic muscle spindle inputs and musculoskeletal modeling techniques, the research team simulated the proprioceptive process in animals and trained neural networks to solve multiple computational tasks, testing various hypotheses regarding proprioceptive processing. These models were used to predict the neural activity in the cuneate nucleus (CN) and the primary somatosensory cortex (S1) of non-human primates, thereby assessing the effectiveness of various hypotheses in explaining these neural dynamics.

Neural network; Neural dynamics; Proprioception