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Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study.
J Neurosci Methods. 2006 Jan 30; 150(2):202-11.JN

Abstract

One of the most important building blocks of the brain-machine interface (BMI) based on neuronal spike trains is the decoding algorithm, a computational method for the reconstruction of desired information from spike trains. Previous studies have reported that a simple linear filter is effective for this purpose and that no noteworthy gain is achieved from the use of nonlinear algorithms. In order to test this premise, we designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR). Their performances were assessed using multiple neuronal spike trains generated by a biophysical neuron model and by a directional tuning model of the primary motor cortex. The performances of the nonlinear algorithms, in general, were superior. The advantages of using nonlinear algorithms were more profound for cases where false-positive/negative errors occurred in spike trains. When the MLPs were trained using trial-and-error, they often showed disappointing performance comparable to that of the linear filter. The nonlinear SVR showed the highest performance, and this may be due to the superiority of SVR in training and generalization.

Authors+Show Affiliations

Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Kangwon-do, South Korea. khkim@dragon.yonsei.ac.krNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

16099513

Citation

Kim, Kyung Hwan, et al. "Superiority of Nonlinear Mapping in Decoding Multiple Single-unit Neuronal Spike Trains: a Simulation Study." Journal of Neuroscience Methods, vol. 150, no. 2, 2006, pp. 202-11.
Kim KH, Kim SS, Kim SJ. Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study. J Neurosci Methods. 2006;150(2):202-11.
Kim, K. H., Kim, S. S., & Kim, S. J. (2006). Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study. Journal of Neuroscience Methods, 150(2), 202-11.
Kim KH, Kim SS, Kim SJ. Superiority of Nonlinear Mapping in Decoding Multiple Single-unit Neuronal Spike Trains: a Simulation Study. J Neurosci Methods. 2006 Jan 30;150(2):202-11. PubMed PMID: 16099513.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Superiority of nonlinear mapping in decoding multiple single-unit neuronal spike trains: a simulation study. AU - Kim,Kyung Hwan, AU - Kim,Sung Shin, AU - Kim,Sung June, Y1 - 2005/08/15/ PY - 2005/02/25/received PY - 2005/06/15/revised PY - 2005/06/20/accepted PY - 2005/8/16/pubmed PY - 2006/4/14/medline PY - 2005/8/16/entrez SP - 202 EP - 11 JF - Journal of neuroscience methods JO - J Neurosci Methods VL - 150 IS - 2 N2 - One of the most important building blocks of the brain-machine interface (BMI) based on neuronal spike trains is the decoding algorithm, a computational method for the reconstruction of desired information from spike trains. Previous studies have reported that a simple linear filter is effective for this purpose and that no noteworthy gain is achieved from the use of nonlinear algorithms. In order to test this premise, we designed several decoding algorithms based on the linear filter, and two nonlinear mapping algorithms using multilayer perceptron (MLP) and support vector machine regression (SVR). Their performances were assessed using multiple neuronal spike trains generated by a biophysical neuron model and by a directional tuning model of the primary motor cortex. The performances of the nonlinear algorithms, in general, were superior. The advantages of using nonlinear algorithms were more profound for cases where false-positive/negative errors occurred in spike trains. When the MLPs were trained using trial-and-error, they often showed disappointing performance comparable to that of the linear filter. The nonlinear SVR showed the highest performance, and this may be due to the superiority of SVR in training and generalization. SN - 0165-0270 UR - https://www.unboundmedicine.com/medline/citation/16099513/Superiority_of_nonlinear_mapping_in_decoding_multiple_single_unit_neuronal_spike_trains:_a_simulation_study_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0165-0270(05)00216-5 DB - PRIME DP - Unbound Medicine ER -