Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders

Oscar W. Savolainen; Zheng Zhang; Peilong Feng; Timothy G. Constandinou

doi:10.1101/2022.03.25.485863

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Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders

View ORCID ProfileOscar W. Savolainen, View ORCID ProfileZheng Zhang, View ORCID ProfilePeilong Feng, View ORCID ProfileTimothy G. Constandinou

doi: https://doi.org/10.1101/2022.03.25.485863

Oscar W. Savolainen

1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

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For correspondence: o.savolainen18@imperial.ac.uk

Zheng Zhang

1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

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Peilong Feng

1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

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Timothy G. Constandinou

1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK

2UK Dementia Research Institute (UKDRI) Care Research & Technology (CR&T) Centre, based at Imperial College London and the University of Surrey, UK

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https://github.com/Next-Generation-Neural-Interfaces/Hardware-efficient-MUA-compression
https://zenodo.org/record/6265023#.Yj4p9efMLIU

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Posted March 28, 2022.

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Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders

Oscar W. Savolainen, Zheng Zhang, Peilong Feng, Timothy G. Constandinou

bioRxiv 2022.03.25.485863; doi: https://doi.org/10.1101/2022.03.25.485863

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Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders

Oscar W. Savolainen, Zheng Zhang, Peilong Feng, Timothy G. Constandinou

bioRxiv 2022.03.25.485863; doi: https://doi.org/10.1101/2022.03.25.485863

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