Information-geometric measures estimate neural interactions during oscillatory brain states
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Date
2014
Authors
Nie, Yimin
Fellous, Jean-Marc
Tatsuno, Masami
Journal Title
Journal ISSN
Volume Title
Publisher
Frontiers Research Foundation
Abstract
The 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 distributions has recently been applied to spike-train analysis 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 general oscillatory mechanisms, externally driven oscillations and internally 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.
Description
Sherpa Romeo green journal: open access
Keywords
Information geometry , Spikes , Spiking neuron model , Oscillation , Neural networks , Oscillatory brain states
Citation
Nie, Y., Fellous, J., & Tatsuno, M. (2014). Information-geometric measures estimate neural interactions during oscillatory brain states. Frontiers in Neural Circuits, 8:11. doi:10.3389/fncir.2014.00011