Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements
Understanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifra...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2013-10-01
|
| Series: | Frontiers in Neurology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fneur.2013.00158/full |
| _version_ | 1819238408313110528 |
|---|---|
| author | Jing eHu Yi eZheng Jianbo eGao |
| author_facet | Jing eHu Yi eZheng Jianbo eGao |
| author_sort | Jing eHu |
| collection | DOAJ |
| description | Understanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly nonstationary, and Fano factor analysis always indicates long-range correlations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual correlations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a re-setting effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains’ long range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses. |
| first_indexed | 2024-12-23T13:35:45Z |
| format | Article |
| id | doaj.art-440a786afa774f6588fb54466d2650c6 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-23T13:35:45Z |
| publishDate | 2013-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-440a786afa774f6588fb54466d2650c62022-12-21T17:45:01ZengFrontiers Media S.A.Frontiers in Neurology1664-22952013-10-01410.3389/fneur.2013.0015863996Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movementsJing eHu0Yi eZheng1Jianbo eGao2PMB Intelligence LLCPMB Intelligence LLCWright State UnivUnderstanding the causal relation between neural inputs and movements is very important for the success of brain machine interfaces (BMIs). In this study, we analyze 104 neurons’ firings using statistical, information theoretic, and fractal analysis. The latter include Fano factor analysis, multifractal adaptive fractal analysis (MF-AFA), and wavelet multifractal analysis. We find neuronal firings are highly nonstationary, and Fano factor analysis always indicates long-range correlations in neuronal firings, irrespective of whether those firings are correlated with movement trajectory or not, and thus does not reveal any actual correlations between neural inputs and movements. On the other hand, MF-AFA and wavelet multifractal analysis clearly indicate that when neuronal firings are not well correlated with movement trajectory, they do not have or only have weak temporal correlations. When neuronal firings are well correlated with movements, they are characterized by very strong temporal correlations, up to a time scale comparable to the average time between two successive reaching tasks. This suggests that neurons well correlated with hand trajectory experienced a re-setting effect at the start of each reaching task, in the sense that within the movement correlated neurons the spike trains’ long range dependences persisted about the length of time the monkey used to switch between task executions. A new task execution re-sets their activity, making them only weakly correlated with their prior activities on longer time scales. We further discuss the significance of the coalition of those important neurons in executing cortical control of prostheses.http://journal.frontiersin.org/Journal/10.3389/fneur.2013.00158/fullWaveletBrain machine interface (BMI)Fano factorAdaptive fluctuation analysisNeuronal firings |
| spellingShingle | Jing eHu Yi eZheng Jianbo eGao Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements Frontiers in Neurology Wavelet Brain machine interface (BMI) Fano factor Adaptive fluctuation analysis Neuronal firings |
| title | Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements |
| title_full | Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements |
| title_fullStr | Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements |
| title_full_unstemmed | Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements |
| title_short | Long-range temporal correlations, multifractality, and the causal relation between neural inputs and movements |
| title_sort | long range temporal correlations multifractality and the causal relation between neural inputs and movements |
| topic | Wavelet Brain machine interface (BMI) Fano factor Adaptive fluctuation analysis Neuronal firings |
| url | http://journal.frontiersin.org/Journal/10.3389/fneur.2013.00158/full |
| work_keys_str_mv | AT jingehu longrangetemporalcorrelationsmultifractalityandthecausalrelationbetweenneuralinputsandmovements AT yiezheng longrangetemporalcorrelationsmultifractalityandthecausalrelationbetweenneuralinputsandmovements AT jianboegao longrangetemporalcorrelationsmultifractalityandthecausalrelationbetweenneuralinputsandmovements |