Deep learning for neural decoding in motor cortex. (23rd September 2022)

• Record Type: Journal Article
• Authors: Liu, Fangyu; Meamardoost, Saber; Gunawan, Rudiyanto; Komiyama, Takaki; Mewes, Claudia; Zhang, Ying; Hwang, EunJung; Wang, Linbing
• Is Part Of: Journal of neural engineering. Volume 19:Number 5(2022)
• Publication Date: 2022-09-23
• Subjects: deep learning -- neural decoding -- neural signals -- concurrent decoding -- time-delay decoding -- spatiotemporal decoding; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac8fb5
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . Neural decoding is an important tool in neural engineering and neural data analysis. Of various machine learning algorithms adopted for neural decoding, the recently introduced deep learning is promising to excel. Therefore, we sought to apply deep learning to decode movement trajectories from the activity of motor cortical neurons. Approach . In this paper, we assessed the performance of deep learning methods in three different decoding schemes, concurrent, time-delay, and spatiotemporal. In the concurrent decoding scheme where the input to the network is the neural activity coincidental to the movement, deep learning networks including artificial neural network (ANN) and long-short term memory (LSTM) were applied to decode movement and compared with traditional machine learning algorithms. Both ANN and LSTM were further evaluated in the time-delay decoding scheme in which temporal delays are allowed between neural signals and movements. Lastly, in the spatiotemporal decoding scheme, we trained convolutional neural network (CNN) to extract movement information from images representing the spatial arrangement of neurons, their activity, and connectomes (i.e. the relative strengths of connectivity between neurons) and combined CNN and ANN to develop a hybrid spatiotemporal network. To reveal the input features of the CNN in the hybrid network that deep learning discovered for movement decoding, we performed a sensitivity analysis and identified specific regions in the spatial domain. Main results . Deep learning networks (ANN and LSTM) outperformed traditional machine learning algorithms in the concurrent decoding scheme. The results of ANN and LSTM in the time-delay decoding scheme showed that including neural data from time points preceding movement enabled decoders to perform more robustly when the temporal relationship between the neural activity and movement dynamically changes over time. In the spatiotemporal decoding scheme, the hybrid spatiotemporal network containing the concurrent ANN decoder outperformed single-network concurrent decoders. Significance . Taken together, our study demonstrates that deep learning could become a robust and effective method for the neural decoding of behavior.