Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings
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Abstract (arXiv)
This paper presents an efficient deep learning solution for decoding motor movements from neural recordings in non-human primates. An Autoencoder Gated Recurrent Unit (AEGRU) model was adopted as the model architecture for this task. The autoencoder is only used during the training stage to achieve better generalization. Together with the preprocessing techniques, our model achieved 0.71 R2 score, surpassing the baseline models in Neurobench and is ranked first for R2 in the IEEE BioCAS 2024 Grand Challenge on Neural Decoding. Model pruning is also applied leading to a reduction of 41.4% of the multiply-accumulate (MAC) operations with little change in the R2 score compared to the unpruned model.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 29 Oct 2024.
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References · 7
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
- 2023NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systemscited by 27
- 2021Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsitycited by 20
- 2019Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Networkcited by 6
- 2020Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Acceleratorcited by 3
- 2023Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfacescited by 3
- 2018BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulationcited by 2
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