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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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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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