Information theoretical estimators of tuning depth and time delay for motor cortex neurons

Yiwen Wang, Justin C. Sanchez, Jose C. Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Knowledge of neural receptive fields helps decode animal's movement from neural activity in motor brain machine interfaces (BMI). The traditional tuning depth is a coarse metric to evaluate the neuron's tuning properties. The major difficulty of the tuning depth is that it does not allow for a direct comparison among neurons that have very different firing rates. An information theoretic tuning depth based on mutual information is proposed to evaluate how neural spikes encode the kinematic direction. This metric is scale invariant and makes the tuning analysis comparable among not only the cortical areas but also among kinematics vectors. It can help identify candidate neuron subsets to reduce the complexity of analysis and decoding in BMI. The application of the metric on neural encoding modeling also provides a way to estimate time delays in motor cortical neural activity.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
Pages502-505
Number of pages4
DOIs
StatePublished - Sep 25 2007
Event3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States
Duration: May 2 2007May 5 2007

Publication series

NameProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering

Other

Other3rd International IEEE EMBS Conference on Neural Engineering
CountryUnited States
CityKohala Coast, HI
Period5/2/075/5/07

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Neuroscience (miscellaneous)

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  • Cite this

    Wang, Y., Sanchez, J. C., & Principe, J. C. (2007). Information theoretical estimators of tuning depth and time delay for motor cortex neurons. In Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (pp. 502-505). [4227324] (Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/CNE.2007.369719