Information-geometric measures estimate neural interactions during oscillatory brain states

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dc.contributor.authorNie, Yimin
dc.contributor.authorFellous, Jean-Marc
dc.contributor.authorTatsuno, Masami
dc.date.accessioned2016-11-17T19:15:41Z
dc.date.available2016-11-17T19:15:41Z
dc.date.issued2014
dc.descriptionSherpa Romeo green journal: open accessen_US
dc.description.abstractThe characterization of functional network structures among multiple neurons is essential to understanding neural information processing .Information geometry (IG) ,a theory developed for investigating a space of probability distribution shas recently been applied to spike-train analys is and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single-and pairwise-IG measures were influenced by oscillatory neural activity. Two genera loscillatory mechanisms, externally driven oscillations andi nternally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.en_US
dc.description.peer-reviewYesen_US
dc.identifier.citationNie, Y., Fellous, J., & Tatsuno, M. (2014). Information-geometric measures estimates neural interactions during oscillatory brain states. Frontiers in Neural Circuits, 8(11). doi:10.3389/fncir.2014.00011en_US
dc.identifier.urihttps://hdl.handle.net/10133/4703
dc.language.isoen_CAen_US
dc.publisherFrontiers Research Foundationen_US
dc.publisher.departmentDepartment of Neuroscienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.publisher.institutionUniversity of Lethbridgeen_US
dc.publisher.institutionUniversity of Arizonaen_US
dc.subjectInformation geometryen_US
dc.subjectSpikesen_US
dc.subjectSpiking neuron modelen_US
dc.subjectOscillationen_US
dc.subjectNeural networksen_US
dc.titleInformation-geometric measures estimate neural interactions during oscillatory brain statesen_US
dc.typeArticleen_US
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