A neural data lossless compression scheme based on spatial and temporal prediction
A neural data lossless compression scheme based on spatial and temporal prediction
复制标题
DOI：
10.1109/biocas.2017.8325196
复制
发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
Matteo Pagin;M. Ortmanns
中科院分区：
文献类型：
--
作者：
Matteo Pagin;M. Ortmanns
本文提出了一种细胞外记录的无损压缩方案。线性神经网络（LNN）用于根据信号的过去样本和来自其相邻通道的数据来预测信号的未来值。传输信号和预测值之间的差，实现无损压缩。LNN可以有效地用于利用神经信号中的空间和时间相关性，与无损增量压缩相比，实现约2倍的压缩比（CR）。还发现，在相邻通道的小组上使用压缩器有助于在相同量的存储器和乘法的情况下实现更高的CR。
A lossless compression scheme for extracellular recordings is presented in this paper. A linear neural network (LNN) is used to predict future values of the signal based on its past samples and data from its neighboring channels. The difference between the signal and the predicted value is transmitted achieving lossless compression. The LNN can effectively be used to exploit spatial and temporal correlation in neural signals, achieving about 2 times more compress ratio (CR) when compared to lossless delta compression. It is also found that using the compressor on small groups of neighboring channels helps achieving higher CR for the same amount of memory and multiplications.