P261 Properteis of intermittent synchrony of gamma rhythm oscillations
Leonid L Rubchinsky*1,2,Quynh-Anh Nguyen3
1Department of Mathematical Sciences, Indiana University Indianapolis, Indianapolis, IN, USA
2Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, US
3Department of Mathematical Sciences, University of Indianapolis, Indianapolis, IN, USA
*Email:lrubchin@iu.edu
Introduction
Synchronization of oscillations of neural activity is implied to be important for a variety of neural phenomena. Most of the studies consider time-averaged measures of synchrony such as phase-locking strength. However, if two signals have some degree of phase locking, it is possible to explore synchrony properties beyond the average phase-locking strength and to study whether the oscillations are close to synchronous state or not at any time (during any oscillatory cycle) [1]. Thus, it is possible to characterize temporal patterning of neural synchrony (e.g. many short desynchronizations vs a few long desynchronizations), which may vary independently of the average synchrony strength [2].
Methods
To study how the properties of the temporal variability of synchronized oscillations are affected by the network properties, we consider populations of model neurons exhibiting pyramidal-interneuron gamma rhythm and apply the same time-series analysis techniques for characterization of temporal synchrony patterning as the ones used in the earlier experimental studies [1,2].
Results
Variation of synaptic strength affects the strength of time-average phase-locking between the networks. However, this variation of synaptic strength also affects the temporal patterning of the synchrony, altering the distribution of the durations of the desynchronizations (similar to the earlier studies of minimal models [3,4]). While synaptic strength affects both synchrony level and its temporal patterning, these effects can be independent of each other: the former can be practically fixed, while the latter may vary. Furthermore, the impacts of the long-range and local synapses tend to be the opposite. Shortening the desynchronization durations tends to be achieved with weakening of long-range synapses and strengthening local synapses.
Discussion
Changes in the temporal patterning of the synchronization of oscillations may potentially affect how the networks are processing the external signals [4,5]. Frequent vs. rare switching between synchronized and desynchronized dynamics may lead to functionally different outcomes even though the average synchrony level between the networks is the same. Synaptic strength changes have thus potential to affect the responses of the neural circuits not only via the average synchrony strength, but also via the more subtle changes, such as altering the temporal patterning of synchronized dynamics, pointing to the potential importance of studying these phenomena.
Acknowledgements
References
1. Ahn, S., & Rubchinsky, L. L. (2013). Chaos,23, 013138. https://doi.org/10.1063/1.4794793
2. Ahn, S., Zauber, S. E., Witt, T., et al. (2018).Clinical Neurophysiology, 129, 842-844. https://doi.org/10.1016/j.clinph.2018.01.063
3. Ahn, S., & Rubchinsky, L. L. (2017).Frontiers in Computational Neuroscience, 11, 44. https://doi.org/10.3389/fncom.2017.00044
4. Nguyen, Q. A., & Rubchinsky, L. L. (2021).Chaos, 31, 043133. https://doi.org/10.1063/5.0042451
5. Nguyen, Q. A., & Rubchinsky, L. L. (2024).Cognitive Neurodynamics, 18, 3821-837. https://doi.org/10.1007/s11571-024-10150-9
