Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders - Data and Results
Oscar W. Savolainen, Zheng Zhang, Peilong Feng, Timothy G. Constandinou · Zenodo (CERN European Organization for Nuclear Research) · 2022
Data and Results associated with journal article: "Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders", authors: Oscar W. Savolainen, Zheng Zhang, Peilong Feng, Timothy Constandinou. Data, formatted for this work as .mat files, originally generously provided for the public by: - Flint dataset: https://pubmed.ncbi.nlm.nih.gov/22733013/ - Sabes dataset: https://zenodo.org/record/3854034#.Yhf5MejP3IV - Brochier dataset: https://www.nature.com/articles/sdata201855#data-citations Results: - Analysed behavioral decoding performance (BDP) results (.pkl) files - Bit Rate (BR) compression results Associated code and link to journal article @ https://github.com/Next-Generation-Neural-Interfaces/Hardware-efficient-MUA-compression