Ascertaining the importance of neurons to develop better brain-machine interfaces

Justin C. Sanchez, Jose M. Carmena, Mikhail A. Lebedev, Miguel A.L. Nicolelis, John G. Harris, Jose C. Principe

Research output: Contribution to journalArticle

85 Scopus citations

Abstract

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.

Original languageEnglish (US)
Pages (from-to)943-953
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume51
Issue number6
DOIs
StatePublished - Jun 1 2004

Keywords

  • Brain-machine interface
  • Cosine tuning
  • Information in neural ensembles
  • Sensitivity-based model pruning

ASJC Scopus subject areas

  • Biomedical Engineering

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    Sanchez, J. C., Carmena, J. M., Lebedev, M. A., Nicolelis, M. A. L., Harris, J. G., & Principe, J. C. (2004). Ascertaining the importance of neurons to develop better brain-machine interfaces. IEEE Transactions on Biomedical Engineering, 51(6), 943-953. https://doi.org/10.1109/TBME.2004.827061