Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes
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Wilson_Kernel density.pdf
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Author(s)
Sodkomkham, Danaipat
Ciliberti, Davide
Wilson, Matthew A.
Fukui, Ken-ichi
Moriyama, Koichi
Numao, Masayuki
Kloosterman, Fabian
Date Issued
September 2015
Journal
Knowledge-Based Systems
Publisher
Elsevier
Citation
Sodkomkham, Danaipat, Davide Cilibertib, Matthew A. Wilsond, Ken-ichi Fukuia, Koichi Moriyamaa, Masayuki Numaoa, and Fabian Kloosterman. "Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes." Knowledge-Based Systems 94 (15 February 2016), pp.1-12.
Version
Final published version
Abstract
To gain a better understanding of how neural ensembles communicate and process information, neural decoding algorithms are used to extract information encoded in their spiking activity. Bayesian decoding is one of the most used neural population decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian decoding can be implemented without the intermediate step of sorting spike waveforms into groups of single units. Here we extend the approach in order to make it suitable for online encoding/decoding scenarios that require real-time decoding such as brain-machine interfaces. We propose an online algorithm for the Bayesian decoding that reduces the time required for decoding neural populations, resulting in a real-time capable decoding framework. More specifically, we improve the speed of the probability density estimation step, which is the most essential and the most expensive computation of the spike-sorting-less decoding process, by developing a kernel density compression algorithm. In contrary to existing online kernel compression techniques, rather than optimizing for the minimum estimation error caused by kernels compression, the proposed method compresses kernels on the basis of the distance between the merging component and its most similar neighbor. Thus, without costly optimization, the proposed method has very low compression latency with a small and manageable estimation error. In addition, the proposed bandwidth matching method for Gaussian kernels merging has an interesting mathematical property whereby optimization in the estimation of the probability density function can be performed efficiently, resulting in a faster decoding speed. We successfully applied the proposed kernel compression algorithm to the Bayesian decoding framework to reconstruct positions of a freely moving rat from hippocampal unsorted spikes, with significant improvements in the decoding speed and acceptable decoding error.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/j.knosys.2015.09.013
