Methods Inf Med 2007; 46(02): 147-150
DOI: 10.1055/s-0038-1625396
Original Article
Schattauer GmbH

Efficient Signal Processing of Multineuronal Activities for Neural Interface and Prosthesis

H. Kaneko
1   National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
,
H. Tamura
2   Graduate School of Frontier Biosciences, Osaka University, Toyonaka, Osaka, Japan
,
T. Kawashima
3   Department of Electrical and Electronic Engineering, Toyohashi University of Technology, Toyohashi, Aichi, Japan
,
S. S. Suzuki
1   National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan
,
I. Fujita
2   Graduate School of Frontier Biosciences, Osaka University, Toyonaka, Osaka, Japan
› Author Affiliations
Further Information

Publication History

Publication Date:
11 January 2018 (online)

Summary

Objectives : Multineuronal spike trains must be efficiently decoded in order to utilize them for controlling artificial limbs and organs. Here we evaluated the efficiency of pooling (averaging) and combining (vectorizing) activities of multiple neurons for decoding neuronal information.

Methods : Multineuronal activities in the monkey inferior temporal (IT) cortex were obtained by classifying spikes of constituent neurons from multichannel data recorded with a multisite microelectrode. We compared pooling and combining procedures for the amount of visual information transferred by neurons, and for the success rate of stimulus estimation based on neuronal activities in each trial.

Results : Both pooling and combining activities of multiple neurons increased the amount of information and the success rate with the number of neurons. However, the degree of improvement obtained by increasing the number of neurons was higher when combining activities as opposed to pooling them.

Conclusion: Combining the activities of multiple neurons is more efficient than pooling them for obtaining a precise interpretation of neuronal signals.

 
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