# Leveraging Recurrent Neural Networks for Predicting Motor Movements from Primate Motor Cortex Neural Recordings

arXiv: https://arxiv.org/abs/2410.22283 · HTML: https://mlsanity.com/paper/2410.22283/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity](https://mlsanity.com/paper/2108.02297/) (2021)
- [Decoding Hand Kinematics from Local Field Potentials Using Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1901.00708/) (2019)
- [Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator](https://mlsanity.com/paper/2002.03197/) (2020)
- [Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces](https://mlsanity.com/paper/2312.15889/) (2023)
- [BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation](https://mlsanity.com/paper/1812.09835/) (2018)

## Built on by (most cited first, 1 of 1)

- [Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity](https://mlsanity.com/paper/2506.13400/) (2025)

## 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.