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Dynamic analyses of information encoding in neural ensembles.
Neural Comput. 2004 Feb; 16(2):277-307.NC

Abstract

Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

Authors+Show Affiliations

Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital/Harvard Medical School, Boston, MA 02114, USA. barbieri@neurostat.mgh.harvard.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

15006097

Citation

Barbieri, Riccardo, et al. "Dynamic Analyses of Information Encoding in Neural Ensembles." Neural Computation, vol. 16, no. 2, 2004, pp. 277-307.
Barbieri R, Frank LM, Nguyen DP, et al. Dynamic analyses of information encoding in neural ensembles. Neural Comput. 2004;16(2):277-307.
Barbieri, R., Frank, L. M., Nguyen, D. P., Quirk, M. C., Solo, V., Wilson, M. A., & Brown, E. N. (2004). Dynamic analyses of information encoding in neural ensembles. Neural Computation, 16(2), 277-307.
Barbieri R, et al. Dynamic Analyses of Information Encoding in Neural Ensembles. Neural Comput. 2004;16(2):277-307. PubMed PMID: 15006097.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Dynamic analyses of information encoding in neural ensembles. AU - Barbieri,Riccardo, AU - Frank,Loren M, AU - Nguyen,David P, AU - Quirk,Michael C, AU - Solo,Victor, AU - Wilson,Matthew A, AU - Brown,Emery N, PY - 2004/3/10/pubmed PY - 2004/4/2/medline PY - 2004/3/10/entrez SP - 277 EP - 307 JF - Neural computation JO - Neural Comput VL - 16 IS - 2 N2 - Neural spike train decoding algorithms and techniques to compute Shannon mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine interfaces. Developing optimal strategies to design decoding algorithms and compute mutual information are therefore important problems in computational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the entropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the performance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our previous results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted. SN - 0899-7667 UR - https://www.unboundmedicine.com/medline/citation/15006097/Dynamic_analyses_of_information_encoding_in_neural_ensembles_ L2 - https://direct.mit.edu/neco/article-lookup/doi/10.1162/089976604322742038 DB - PRIME DP - Unbound Medicine ER -