Paper Info
Title
Multi-Resolution Multi-Trial Sparse Classification Model For Decoding Visual Memories From Hippocampal Spikes In Human
Abstract
To understand how memories are encoded in the hippocampus, we build memory decoding models to classify visual memories based on hippocampal activities in human. Model inputs are spatio-temporal patterns of spikes recorded in the hippocampal CA3 and CA1 regions of epilepsy patients performing a delayed match-to-sample (DMS) task. Model outputs are binary labels indicating categories and features of sample images. To solve the super high-dimensional estimation problem with short data length, we develop a multi-trial, sparse model estimation method utilizing B-spline basis functions with a large range of temporal resolutions and a regularized logistic classifier. Results show that this model can effectively avoid overfitting and provide significant amount of prediction to memory categories and features using very limited number of data points. Stable estimation of sparse classification function matrices for each label can be obtained with this multiresolution, multi-trial procedure. These classification models can be used not only to predict memory contents, but also to design optimal spatio-temporal patterns for eliciting specific memories in the hippocampus, and thus have important implications to the development of hippocampal memory prostheses.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037006
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Data point,Pattern recognition,Computer science,Basis function,Artificial intelligence,Decoding methods,Overfitting,Classifier (linguistics),Hippocampal formation,Binary number,Temporal lobe
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
2
5
Name
Order
Citations
PageRank
Dong Song120234.25
Xiwei She200.68
Robert E Hampson310512.12
Sam A Deadwyler49810.89
theodore w berger538087.26