Neural decoding involves correlating signals acquired from the brain to
variables in the physical world like limb movement or robot control in Brain
Machine Interfaces. In this context, this work starts from a specific
pre-existing dataset of neural recordings from monkey motor cortex and presents
a Deep Learning-based approach to the decoding of neural signals for grasp type
classification. Specifically, we propose here an approach that exploits LSTM
networks to classify time series containing neural data (i.e., spike trains)
into classes representing the object being grasped. The main goal of the
presented approach is to improve over state-of-the-art decoding accuracy
without relying on any prior neuroscience knowledge, and leveraging only the
capability of deep learning models to extract correlations from data. The paper
presents the results achieved for the considered dataset and compares them with
previous works on the same dataset, showing a significant improvement in
classification accuracy, even if considering simulated real-time decoding.

该研究论文采用深度学习技术，基于猴子运动皮层的神经记录数据，通过利用 LSTM 网络对神经信号进行解码以实现物体抓取类型分类，取得了显著的分类准确率提升，并比较了该方法与其他先前方法的表现。