Dynamic analyses of information encoding in neural ensembles. Barbieri, R., Frank, M, L., Nguyen, P, D., Quirk, C, M., Solo, V., Wilson, A, M., Brown, & N, E. Neural Comput., 16(2):277--307, February, 2004. ![Dynamic analyses of information encoding in neural ensembles. [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{ Barb_2004_277,
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.},
added-at = {2009-06-03T11:20:58.000+0200},
author = {Barbieri, Riccardo and Frank, Loren M and Nguyen, David P and Quirk, Michael C and Solo, Victor and Wilson, Matthew A and Brown, Emery N},
biburl = {http://www.bibsonomy.org/bibtex/255b36022e10a07e42f0339963a3ba074/hake},
description = {The whole bibliography file I use.},
doi = {10.1162/089976604322742038},
file = {Barb_2004_277.pdf:Barb_2004_277.pdf:PDF},
interhash = {068c5ef6cfc1453132764740b244f6a5},
intrahash = {55b36022e10a07e42f0339963a3ba074},
journal = {Neural Comput.},
keywords = {(Computer), 15006097 Action Algorithms, Animals, Behavior, Comparative Computer-Assisted, Exploratory Gov't, Hippocampus, Long-Evans, Nerve Net, Networks Neural Neurons, Non-P.H.S., Non-U.S. P.H.S., Potentials, Processes, Processing, Rats, Reaction Reproducibility Research Results, Signal Stochastic Study, Support, Synaptic Time, Transmission, U.S. of},
month = {February},
number = {2},
pages = {277--307},
pmid = {15006097},
timestamp = {2006.07.05},
title = {Dynamic analyses of information encoding in neural ensembles.},
url = {http://dx.doi.org/10.1162/089976604322742038},
volume = {16},
year = {2004}
}
Downloads: 0
{"_id":"3DiZZhcpJj6pWaA3G","bibbaseid":"barbieri-frank-m-nguyen-p-quirk-c-solo-wilson-a-brown-n-dynamicanalysesofinformationencodinginneuralensembles-2004","downloads":0,"creationDate":"2015-06-23T19:35:51.899Z","title":"Dynamic analyses of information encoding in neural ensembles.","author_short":["Barbieri, R.","Frank","M, L.","Nguyen","P, D.","Quirk","C, M.","Solo, V.","Wilson","A, M.","Brown","N, E."],"year":2004,"bibtype":"article","biburl":"http://www.bibsonomy.org/bib/author/emery?items=1000","bibdata":{"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.","added-at":"2009-06-03T11:20:58.000+0200","author":["Barbieri, Riccardo","Frank","M, Loren","Nguyen","P, David","Quirk","C, Michael","Solo, Victor","Wilson","A, Matthew","Brown","N, Emery"],"author_short":["Barbieri, R.","Frank","M, L.","Nguyen","P, D.","Quirk","C, M.","Solo, V.","Wilson","A, M.","Brown","N, E."],"bibtex":"@article{ Barb_2004_277,\n abstract = {Neural spike train decoding algorithms and techniques to compute Shannon\n\tmutual information are important methods for analyzing how neural\n\tsystems represent biological signals. Decoding algorithms are also\n\tone of several strategies being used to design controls for brain-machine\n\tinterfaces. Developing optimal strategies to design decoding algorithms\n\tand compute mutual information are therefore important problems in\n\tcomputational neuroscience. We present a general recursive filter\n\tdecoding algorithm based on a point process model of individual neuron\n\tspiking activity and a linear stochastic state-space model of the\n\tbiological signal. We derive from the algorithm new instantaneous\n\testimates of the entropy, entropy rate, and the mutual information\n\tbetween the signal and the ensemble spiking activity. We assess the\n\taccuracy of the algorithm by computing, along with the decoding error,\n\tthe true coverage probability of the approximate 0.95 confidence\n\tregions for the individual signal estimates. We illustrate the new\n\talgorithm by reanalyzing the position and ensemble neural spiking\n\tactivity of CA1 hippocampal neurons from two rats foraging in an\n\topen circular environment. We compare the performance of this algorithm\n\twith a linear filter constructed by the widely used reverse correlation\n\tmethod. The median decoding error for Animal 1 (2) during 10 minutes\n\tof open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0)\n\tbits, the median information was 9.4 (9.4) bits, and the true coverage\n\tprobability for 0.95 confidence regions was 0.67 (0.75) using 34\n\t(32) neurons. These findings improve significantly on our previous\n\tresults and suggest an integrated approach to dynamically reading\n\tneural codes, measuring their properties, and quantifying the accuracy\n\twith which encoded information is extracted.},\n added-at = {2009-06-03T11:20:58.000+0200},\n author = {Barbieri, Riccardo and Frank, Loren M and Nguyen, David P and Quirk, Michael C and Solo, Victor and Wilson, Matthew A and Brown, Emery N},\n biburl = {http://www.bibsonomy.org/bibtex/255b36022e10a07e42f0339963a3ba074/hake},\n description = {The whole bibliography file I use.},\n doi = {10.1162/089976604322742038},\n file = {Barb_2004_277.pdf:Barb_2004_277.pdf:PDF},\n interhash = {068c5ef6cfc1453132764740b244f6a5},\n intrahash = {55b36022e10a07e42f0339963a3ba074},\n journal = {Neural Comput.},\n keywords = {(Computer), 15006097 Action Algorithms, Animals, Behavior, Comparative Computer-Assisted, Exploratory Gov't, Hippocampus, Long-Evans, Nerve Net, Networks Neural Neurons, Non-P.H.S., Non-U.S. P.H.S., Potentials, Processes, Processing, Rats, Reaction Reproducibility Research Results, Signal Stochastic Study, Support, Synaptic Time, Transmission, U.S. of},\n month = {February},\n number = {2},\n pages = {277--307},\n pmid = {15006097},\n timestamp = {2006.07.05},\n title = {Dynamic analyses of information encoding in neural ensembles.},\n url = {http://dx.doi.org/10.1162/089976604322742038},\n volume = {16},\n year = {2004}\n}","bibtype":"article","biburl":"http://www.bibsonomy.org/bibtex/255b36022e10a07e42f0339963a3ba074/hake","description":"The whole bibliography file I use.","doi":"10.1162/089976604322742038","file":"Barb_2004_277.pdf:Barb_2004_277.pdf:PDF","id":"Barb_2004_277","interhash":"068c5ef6cfc1453132764740b244f6a5","intrahash":"55b36022e10a07e42f0339963a3ba074","journal":"Neural Comput.","key":"Barb_2004_277","keywords":"(Computer), 15006097 Action Algorithms, Animals, Behavior, Comparative Computer-Assisted, Exploratory Gov't, Hippocampus, Long-Evans, Nerve Net, Networks Neural Neurons, Non-P.H.S., Non-U.S. P.H.S., Potentials, Processes, Processing, Rats, Reaction Reproducibility Research Results, Signal Stochastic Study, Support, Synaptic Time, Transmission, U.S. of","month":"February","number":"2","pages":"277--307","pmid":"15006097","timestamp":"2006.07.05","title":"Dynamic analyses of information encoding in neural ensembles.","type":"article","url":"http://dx.doi.org/10.1162/089976604322742038","volume":"16","year":"2004","bibbaseid":"barbieri-frank-m-nguyen-p-quirk-c-solo-wilson-a-brown-n-dynamicanalysesofinformationencodinginneuralensembles-2004","role":"author","urls":{"Paper":"http://dx.doi.org/10.1162/089976604322742038"},"keyword":["(Computer)","15006097 Action Algorithms","Animals","Behavior","Comparative Computer-Assisted","Exploratory Gov't","Hippocampus","Long-Evans","Nerve Net","Networks Neural Neurons","Non-P.H.S.","Non-U.S. P.H.S.","Potentials","Processes","Processing","Rats","Reaction Reproducibility Research Results","Signal Stochastic Study","Support","Synaptic Time","Transmission","U.S. of"],"downloads":0},"search_terms":["dynamic","analyses","information","encoding","neural","ensembles","barbieri","frank","m","nguyen","p","quirk","c","solo","wilson","a","brown","n"],"keywords":["(computer)","15006097 action algorithms","animals","behavior","comparative computer-assisted","exploratory gov't","hippocampus","long-evans","nerve net","networks neural neurons","non-p.h.s.","non-u.s. p.h.s.","potentials","processes","processing","rats","reaction reproducibility research results","signal stochastic study","support","synaptic time","transmission","u.s. of"],"authorIDs":[],"dataSources":["suxCQ2gq5uDsWk5Zb"]}