A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings
Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals...
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| Published in | BMC neuroscience Vol. 10; no. 1; p. 81 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BioMed Central Ltd
16.07.2009
BioMed Central BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2202 1471-2202 |
| DOI | 10.1186/1471-2202-10-81 |
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| Abstract | Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.
Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.
The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. |
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| AbstractList | BACKGROUND: Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. RESULTS: Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. CONCLUSION: The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.BACKGROUNDInformation theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.RESULTSHere we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies.The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.CONCLUSIONThe new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code. |
| ArticleNumber | 81 |
| Audience | Academic |
| Author | Whittingstall, Kevin Logothetis, Nikos K Singh, Vanessa Magri, Cesare Panzeri, Stefano |
| AuthorAffiliation | 3 Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9PT, UK 1 Italian Institute of Technology, Department of Robotics, Brain and Cognitive Sciences, I-16163 Genoa, Italy 2 Max Planck Institute for Biological Cybernetics, D-72076 Tübingen, Germany |
| AuthorAffiliation_xml | – name: 3 Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9PT, UK – name: 1 Italian Institute of Technology, Department of Robotics, Brain and Cognitive Sciences, I-16163 Genoa, Italy – name: 2 Max Planck Institute for Biological Cybernetics, D-72076 Tübingen, Germany |
| Author_xml | – sequence: 1 givenname: Cesare surname: Magri fullname: Magri, Cesare – sequence: 2 givenname: Kevin surname: Whittingstall fullname: Whittingstall, Kevin – sequence: 3 givenname: Vanessa surname: Singh fullname: Singh, Vanessa – sequence: 4 givenname: Nikos K surname: Logothetis fullname: Logothetis, Nikos K – sequence: 5 givenname: Stefano surname: Panzeri fullname: Panzeri, Stefano |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19607698$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2009 BioMed Central Ltd. 2009 Magri et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2009 Magri et al; licensee BioMed Central Ltd. 2009 Magri et al; licensee BioMed Central Ltd. |
| Copyright_xml | – notice: COPYRIGHT 2009 BioMed Central Ltd. – notice: 2009 Magri et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. – notice: Copyright © 2009 Magri et al; licensee BioMed Central Ltd. 2009 Magri et al; licensee BioMed Central Ltd. |
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| DOI | 10.1186/1471-2202-10-81 |
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| Snippet | Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of... Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the... Abstract Background: Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use... BACKGROUND: Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the... Abstract Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use... |
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| SubjectTerms | Action potentials (Electrophysiology) Algorithms Animals Brain Brain - physiology Computer Simulation Electrodes Electroencephalography Electronic Data Processing Electrophysiology Information Theory Macaca Measurement Models, Neurological Neurons - physiology Neurophysiology Physiological aspects Programming languages Robotics Signal Processing, Computer-Assisted Software Standard deviation Studies |
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| Title | A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings |
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