  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.
