Title: Bayesian decoding using unsorted spikes in the rat hippocampus
Authors: Kloosterman, Fabian ×
Layton, Stuart P
Chen, Zhe
Wilson, Matthew A #
Issue Date: Jan-2014
Publisher: The Society
Series Title: Journal of Neurophysiology vol:111 issue:1 pages:217-27
Article number: 10.1152/jn.01046.2012
Abstract: A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariates of interest and avoids accumulation of spike sorting errors. Our decoding paradigm is nonparametric, encoding model-free for representing stimuli, and extracts information from all available spikes and their waveform features. We apply the proposed Bayesian decoding algorithm to a position reconstruction task for freely behaving rats based on tetrode recordings of rat hippocampal neuronal activity. Our detailed decoding analyses demonstrate that our approach is efficient and better utilizes the available information in the nonsortable hash than the standard sorting-based decoding algorithm. Our approach can be adapted to an online encoding/decoding framework for applications that require real-time decoding, such as brain-machine interfaces.
ISSN: 0022-3077
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Biological Psychology
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science