Decoding finger velocity from cortical spike trains with recurrent spiking neural networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909303312482304 |
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| author | Liu, Tengjun Gygax, Julia Rossbroich, Julian Chua, Yansong Zhang, Shaomin Zenke, Friedemann |
| author_facet | Liu, Tengjun Gygax, Julia Rossbroich, Julian Chua, Yansong Zhang, Shaomin Zenke, Friedemann |
| contents | Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection risk, which calls for fully implanted systems. Such systems, however, must meet strict latency and energy constraints while providing reliable decoding performance. While recurrent spiking neural networks (RSNNs) are ideally suited for ultra-low-power, low-latency processing on neuromorphic hardware, it is unclear whether they meet the above requirements. To address this question, we trained RSNNs to decode finger velocity from cortical spike trains (CSTs) of two macaque monkeys. First, we found that a large RSNN model outperformed existing feedforward spiking neural networks (SNNs) and artificial neural networks (ANNs) in terms of their decoding accuracy. We next developed a tiny RSNN with a smaller memory footprint, low firing rates, and sparse connectivity. Despite its reduced computational requirements, the resulting model performed substantially better than existing SNN and ANN decoders. Our results thus demonstrate that RSNNs offer competitive CST decoding performance under tight resource constraints and are promising candidates for fully implanted ultra-low-power BMIs with the potential to revolutionize patient care. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_01762 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding finger velocity from cortical spike trains with recurrent spiking neural networks Liu, Tengjun Gygax, Julia Rossbroich, Julian Chua, Yansong Zhang, Shaomin Zenke, Friedemann Neurons and Cognition Human-Computer Interaction Machine Learning Neural and Evolutionary Computing Invasive cortical brain-machine interfaces (BMIs) can significantly improve the life quality of motor-impaired patients. Nonetheless, externally mounted pedestals pose an infection risk, which calls for fully implanted systems. Such systems, however, must meet strict latency and energy constraints while providing reliable decoding performance. While recurrent spiking neural networks (RSNNs) are ideally suited for ultra-low-power, low-latency processing on neuromorphic hardware, it is unclear whether they meet the above requirements. To address this question, we trained RSNNs to decode finger velocity from cortical spike trains (CSTs) of two macaque monkeys. First, we found that a large RSNN model outperformed existing feedforward spiking neural networks (SNNs) and artificial neural networks (ANNs) in terms of their decoding accuracy. We next developed a tiny RSNN with a smaller memory footprint, low firing rates, and sparse connectivity. Despite its reduced computational requirements, the resulting model performed substantially better than existing SNN and ANN decoders. Our results thus demonstrate that RSNNs offer competitive CST decoding performance under tight resource constraints and are promising candidates for fully implanted ultra-low-power BMIs with the potential to revolutionize patient care. |
| title | Decoding finger velocity from cortical spike trains with recurrent spiking neural networks |
| topic | Neurons and Cognition Human-Computer Interaction Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2409.01762 |