Network mechanisms underlying the role of oscillations in cognitive tasks

Abstract

Oscillatory activity robustly correlates with task demands during many cognitive tasks. However, not only are the network mechanisms underlying the generation of these rhythms poorly understood, but it is also still unknown to what extent they may play a functional role, as opposed to being a mere epiphenomenon. Here we study the mechanisms underlying the influence of oscillatory drive on network dynamics related to cognitive processing in simple working memory (WM), and memory recall tasks. Specifically, we investigate how the frequency of oscillatory input interacts with the intrinsic dynamics in networks of recurrently coupled spiking neurons to cause changes of state: the neuronal correlates of the corresponding cognitive process. We find that slow oscillations, in the delta and theta band, are effective in activating network states associated with memory recall. On the other hand, faster oscillations, in the beta range, can serve to clear memory states by resonantly driving transient bouts of spike synchrony which destabilize the activity. We leverage a recently derived set of exact mean-field equations for networks of quadratic integrate-and-fire neurons to systematically study the bifurcation structure in the periodically forced spiking network. Interestingly, we find that the oscillatory signals which are most effective in allowing flexible switching between network states are not smooth, pure sinusoids, but rather burst-like, with a sharp onset. We show that such periodic bursts themselves readily arise spontaneously in networks of excitatory and inhibitory neurons, and that the burst frequency can be tuned via changes in tonic drive. Finally, we show that oscillations in the gamma range can actually stabilize WM states which otherwise would not persist.

Citation

Schmidt, H., Avitabile, D., Montbrió, E., & Roxin, A. (2018). Network mechanisms underlying the role of oscillations in cognitive tasks. PLoS Computational Biology, 14(9), 1-24. https://doi.org/10.1371/journal.pcbi.1006430

• Journal Article Type: Article
• Acceptance Date: Aug 13, 2018
• Online Publication Date: Sep 6, 2018
• Publication Date: Sep 6, 2018
• Deposit Date: Sep 24, 2018
• Publicly Available Date: Sep 24, 2018
• Journal: PLOS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 9
• Article Number: e1006430
• Pages: 1-24
• DOI: https://doi.org/10.1371/journal.pcbi.1006430
• Keywords: Ecology; Modelling and Simulation; Computational Theory and Mathematics; Genetics; Ecology, Evolution, Behavior and Systematics; Molecular Biology; Cellular and Molecular Neuroscience
• Public URL: https://nottingham-repository.worktribe.com/output/1128889
• Publisher URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006430