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Published February 24, 2022 | Version 1.0.0
Journal article Open

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

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

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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