Prefrontal oscillations modulate the propagation of neuronal activity required for working memory
Name
1-s2.0-S1074742720300721-main.pdf
Description
Published version
Size
3 MB
Format
Adobe PDF
Checksum (MD5)
50a0b2e314db077e0ba43dd81af772c8
Author(s)
Sherfey, Jason
Ardid, Salva
Miller, Earl K
Hasselmo, Michael E
Kopell, Nancy J
Date Issued
2020
Journal
Neurobiology of Learning and Memory
Publisher
Elsevier BV
Version
Final published version
Abstract
© 2020 The Author(s) Cognition involves using attended information, maintained in working memory (WM), to guide action. During a cognitive task, a correct response requires flexible, selective gating so that only the appropriate information flows from WM to downstream effectors that carry out the response. In this work, we used biophysically-detailed modeling to explore the hypothesis that network oscillations in prefrontal cortex (PFC), leveraging local inhibition, can independently gate responses to items in WM. The key role of local inhibition was to control the period between spike bursts in the outputs, and to produce an oscillatory response no matter whether the WM item was maintained in an asynchronous or oscillatory state. We found that the WM item that induced an oscillatory population response in the PFC output layer with the shortest period between spike bursts was most reliably propagated. The network resonant frequency (i.e., the input frequency that produces the largest response) of the output layer can be flexibly tuned by varying the excitability of deep layer principal cells. Our model suggests that experimentally-observed modulation of PFC beta-frequency (15–30 Hz) and gamma-frequency (30–80 Hz) oscillations could leverage network resonance and local inhibition to govern the flexible routing of signals in service to cognitive processes like gating outputs from working memory and the selection of rule-based actions. Importantly, we show for the first time that nonspecific changes in deep layer excitability can tune the output gate's resonant frequency, enabling the specific selection of signals encoded by populations in asynchronous or fast oscillatory states. More generally, this represents a dynamic mechanism by which adjusting network excitability can govern the propagation of asynchronous and oscillatory signals throughout neocortex.
MIT Department
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/J.NLM.2020.107228
