Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining
We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-10-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00132/full |
| _version_ | 1818141833516023808 |
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| author | Emiliano eTorre David ePicado-Muino Michael eDenker Christian eBorgelt Sonja eGrün Sonja eGrün |
| author_facet | Emiliano eTorre David ePicado-Muino Michael eDenker Christian eBorgelt Sonja eGrün Sonja eGrün |
| author_sort | Emiliano eTorre |
| collection | DOAJ |
| description | We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of patterns of same signature, i.e. with the same pattern size z and support c. Here, we derive in detail a statistical test with the null-hypothesis of full independence (pattern spectrum filtering, PSF) by comparing the pattern spectrum to the significance spectrum obtained from surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected and yield a low false negative rate. However, this approach is prone to additionally classify patterns as significant which result from chance overlap of real assembly activity and background spiking. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains. |
| first_indexed | 2024-12-11T11:06:10Z |
| format | Article |
| id | doaj.art-c5788505b63f41eeb540d85bd615c986 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-11T11:06:10Z |
| publishDate | 2013-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-c5788505b63f41eeb540d85bd615c9862022-12-22T01:09:41ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-10-01710.3389/fncom.2013.0013257096Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set MiningEmiliano eTorre0David ePicado-Muino1Michael eDenker2Christian eBorgelt3Sonja eGrün4Sonja eGrün5Jülich Research Centre and JARAEuropean Centre for Soft ComputingJülich Research Centre and JARAEuropean Centre for Soft ComputingJülich Research Centre and JARARWTH AachenWe recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of patterns of same signature, i.e. with the same pattern size z and support c. Here, we derive in detail a statistical test with the null-hypothesis of full independence (pattern spectrum filtering, PSF) by comparing the pattern spectrum to the significance spectrum obtained from surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected and yield a low false negative rate. However, this approach is prone to additionally classify patterns as significant which result from chance overlap of real assembly activity and background spiking. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00132/fullData Mininghigher-order correlationsmultiple testingspike patternsspike synchronyneuronal cell assemblies |
| spellingShingle | Emiliano eTorre David ePicado-Muino Michael eDenker Christian eBorgelt Sonja eGrün Sonja eGrün Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining Frontiers in Computational Neuroscience Data Mining higher-order correlations multiple testing spike patterns spike synchrony neuronal cell assemblies |
| title | Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining |
| title_full | Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining |
| title_fullStr | Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining |
| title_full_unstemmed | Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining |
| title_short | Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining |
| title_sort | statistical evaluation of synchronous spike patterns extracted by frequent item set mining |
| topic | Data Mining higher-order correlations multiple testing spike patterns spike synchrony neuronal cell assemblies |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00132/full |
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