Hardware-Efficient Compression of Neural Multi-Unit Activity Using Machine Learning Selected Static Huffman Encoders - Data and Results
- 1. Imperial College London
Description
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
Files
Upload_data_and_results.zip
Files
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Additional details
Funding
- DTP 2016-2017 Imperal College EP/N509486/1
- UK Research and Innovation
- Empowering Next Generation Implantable Neural Interfaces EP/M020975/1
- UK Research and Innovation
- Functional Oxide Reconfigurable Technologies (FORTE): A Programme Grant EP/R024642/1
- UK Research and Innovation