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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
Zheng Zhang
1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Peilong Feng
1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
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
Posted March 28, 2022.
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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