Network mechanisms underlying the role of oscillations in cognitive tasks

Helmut Schmidt*, Daniele Avitabile, Ernest Montbrió, Alex Roxin

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Article numbere1006430
JournalPLoS Computational Biology
Issue number9
Publication statusPublished - 1 Sept 2018
Externally publishedYes


HS and AR acknowledge financial support from the Spanish Ministry of Economics and Competitiveness through the María de Maeztu Programme for Units of Excellence in R&D (MDM- 2014-0445) and grant MTM2015-71509-C2-1-R. EM acknowledges support by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement No. 642563, and project grants from the Spanish Ministry of Economics and Competitiveness, Grants No. PSI2016-75688-P and No. PCIN-2015-127. AR acknowledges a project grant from the Spanish Ministry of Economics and Competitiveness, Grant No. BFU2012-33413. AR has been partially funded by the CERCA programme of the Generalitat de Catalunya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

FundersFunder number
Horizon 2020 Framework Programme
Horizon 2020642563


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