Tags

Type your tag names separated by a space and hit enter

Ascertaining the importance of neurons to develop better brain-machine interfaces.
IEEE Trans Biomed Eng. 2004 Jun; 51(6):943-53.IT

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.

Authors+Show Affiliations

Department of Biomedical Engineering, University of Florida, Room EB 454, Gainesville, FL 32611, USA. justin@cnel.ufl.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Validation Study

Language

eng

PubMed ID

15188862

Citation

Sanchez, Justin C., et al. "Ascertaining the Importance of Neurons to Develop Better Brain-machine Interfaces." IEEE Transactions On Bio-medical Engineering, vol. 51, no. 6, 2004, pp. 943-53.
Sanchez JC, Carmena JM, Lebedev MA, et al. Ascertaining the importance of neurons to develop better brain-machine interfaces. IEEE Trans Biomed Eng. 2004;51(6):943-53.
Sanchez, J. C., Carmena, J. M., Lebedev, M. A., Nicolelis, M. A., Harris, J. G., & Principe, J. C. (2004). Ascertaining the importance of neurons to develop better brain-machine interfaces. IEEE Transactions On Bio-medical Engineering, 51(6), 943-53.
Sanchez JC, et al. Ascertaining the Importance of Neurons to Develop Better Brain-machine Interfaces. IEEE Trans Biomed Eng. 2004;51(6):943-53. PubMed PMID: 15188862.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Ascertaining the importance of neurons to develop better brain-machine interfaces. AU - Sanchez,Justin C, AU - Carmena,Jose M, AU - Lebedev,Mikhail A, AU - Nicolelis,Miguel A L, AU - Harris,John G, AU - Principe,Jose C, PY - 2004/6/11/pubmed PY - 2004/9/2/medline PY - 2004/6/11/entrez SP - 943 EP - 53 JF - IEEE transactions on bio-medical engineering JO - IEEE Trans Biomed Eng VL - 51 IS - 6 N2 - 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. SN - 0018-9294 UR - https://www.unboundmedicine.com/medline/citation/15188862/Ascertaining_the_importance_of_neurons_to_develop_better_brain_machine_interfaces_ L2 - https://doi.org/10.1109/TBME.2004.827061 DB - PRIME DP - Unbound Medicine ER -