An information-theoretic approach for evaluating probabilistic tuning functions of single neurons
Neuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of independent variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as finding the right combination of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2011-03-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00015/full |
| _version_ | 1818011947854987264 |
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| author | Lukas eBrostek Lukas eBrostek Thomas eEggert Seiji eOno Michael J Mustari Ulrich eBüttner Ulrich eBüttner Stefan eGlasauer Stefan eGlasauer Stefan eGlasauer |
| author_facet | Lukas eBrostek Lukas eBrostek Thomas eEggert Seiji eOno Michael J Mustari Ulrich eBüttner Ulrich eBüttner Stefan eGlasauer Stefan eGlasauer Stefan eGlasauer |
| author_sort | Lukas eBrostek |
| collection | DOAJ |
| description | Neuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of independent variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as finding the right combination of independent variables and determining their proper neuronal latencies. Here we present a novel approach of estimating and evaluating such probabilistic tuning functions, which offers a solution for these problems. By maximizing the mutual information between the probability distributions of spike occurrence and the variables, their neuronal latency can be estimated and the dependence of neuronal activity on different combinations of variables can be measured. The method was used to analyze neuronal activity in cortical area MSTd in dependence on signals related to eye and retinal image movement. Comparison with conventional feature detection and regression analysis techniques shows that our method offers distinct advantages, if the dependence does not match the regression model. |
| first_indexed | 2024-04-14T06:14:54Z |
| format | Article |
| id | doaj.art-657bc4b9cf024549ae47d58c4da34699 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-14T06:14:54Z |
| publishDate | 2011-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-657bc4b9cf024549ae47d58c4da346992022-12-22T02:08:14ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882011-03-01510.3389/fncom.2011.000158513An information-theoretic approach for evaluating probabilistic tuning functions of single neuronsLukas eBrostek0Lukas eBrostek1Thomas eEggert2Seiji eOno3Michael J Mustari4Ulrich eBüttner5Ulrich eBüttner6Stefan eGlasauer7Stefan eGlasauer8Stefan eGlasauer9Ludwig-Maximilians-UniversitätLudwig-Maximilians-UniversitätLudwig-Maximilians-UniversitätUniversity of WashingtonUniversity of WashingtonLudwig-Maximilians-UniversitätLudwig-Maximilians-UniversitätLudwig-Maximilians-UniversitätLudwig-Maximilians-UniversitätLudwig-Maximilians-UniversitätNeuronal tuning functions can be expressed by the conditional probability of observing a spike given any combination of independent variables. However, accurately determining such probabilistic tuning functions from experimental data poses several challenges such as finding the right combination of independent variables and determining their proper neuronal latencies. Here we present a novel approach of estimating and evaluating such probabilistic tuning functions, which offers a solution for these problems. By maximizing the mutual information between the probability distributions of spike occurrence and the variables, their neuronal latency can be estimated and the dependence of neuronal activity on different combinations of variables can be measured. The method was used to analyze neuronal activity in cortical area MSTd in dependence on signals related to eye and retinal image movement. Comparison with conventional feature detection and regression analysis techniques shows that our method offers distinct advantages, if the dependence does not match the regression model.http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00015/fullInformation Theorymutual informationMSTdNeuronal LatencyNeuronal Tuning |
| spellingShingle | Lukas eBrostek Lukas eBrostek Thomas eEggert Seiji eOno Michael J Mustari Ulrich eBüttner Ulrich eBüttner Stefan eGlasauer Stefan eGlasauer Stefan eGlasauer An information-theoretic approach for evaluating probabilistic tuning functions of single neurons Frontiers in Computational Neuroscience Information Theory mutual information MSTd Neuronal Latency Neuronal Tuning |
| title | An information-theoretic approach for evaluating probabilistic tuning functions of single neurons |
| title_full | An information-theoretic approach for evaluating probabilistic tuning functions of single neurons |
| title_fullStr | An information-theoretic approach for evaluating probabilistic tuning functions of single neurons |
| title_full_unstemmed | An information-theoretic approach for evaluating probabilistic tuning functions of single neurons |
| title_short | An information-theoretic approach for evaluating probabilistic tuning functions of single neurons |
| title_sort | information theoretic approach for evaluating probabilistic tuning functions of single neurons |
| topic | Information Theory mutual information MSTd Neuronal Latency Neuronal Tuning |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2011.00015/full |
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