Evidence map›Paper›PMID 27927782›Full record

ArticleJournal of neurophysiology2017

Modeling fast and slow gamma oscillations with interneurons of different subtype.

Stephen Keeley, André A Fenton, John Rinzel

Abstract read
In one paragraph

Article in Journal of neurophysiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. Review
  2. Optimal Inhibitory-to-Excitatory Ratio Governs Slow and Fast Oscillations for Enhanced Neural Communication.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  3. Article
  4. Delayed Accumulation of Inhibitory Input Explains Gamma Frequency Variation with Changing Contrast in an Inhibition Stabilized Network.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Article
  5. Article
  6. Firing rate models for gamma oscillations in I-I and E-I networks.Journal of computational neuroscience · 2024
    Article
  7. Article
  8. Article
  9. Article
  10. Regulation of the E/I-balance by the neural matrisome.Frontiers in molecular neuroscience · 2023
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Stephen KeeleyCenter for Neural Science, New York University, New York, New York; and stephenlkeeley@gmail.com.
André A FentonCenter for Neural Science, New York University, New York, New York; and.
John RinzelCenter for Neural Science, New York University, New York, New York; and.

Funding

Translation, Synchrony, and CognitionR01MH099128 · NIMH · NEW YORK UNIVERSITY · PI FENTON, ANDRE ANTONIO · 2013 to 2017
$2.0M
NIMH NIH HHS R01 MH099128
6 · The paper itself

Abstract

Experimental and theoretical studies demonstrate that neuronal gamma oscillations crucially depend on interneurons, but current models do not consider the diversity of known interneuron subtypes. Moreover, in CA1 of the hippocampus, experimental evidence indicates the presence of multiple gamma oscillators, two of which may be coordinated by differing interneuron populations. In this article, we show that models of networks with competing interneuron populations with different postsynaptic effects are sufficient to generate, within CA1, distinct oscillatory regimes. We find that strong mutual inhibition between the interneuron populations permits distinct fast and slow gamma states, whereas weak mutual inhibition generates mixed gamma states. We develop idealized firing rate models to illuminate dynamic properties of these competitive gamma networks, and reinforce these concepts with basic spiking models. The models make several explicit predictions about gamma oscillators in CA1. Specifically, interneurons of different subtype phase-lock to different gamma states, and one population of interneurons is silenced and the other active during fast and slow gamma events. Finally, mutual inhibition between interneuron populations is necessary to generate distinct gamma states. Previous experimental studies indicate that fast and slow gamma oscillations reflect different information processing modes, although it is unclear whether these rhythms are intrinsic or imposed. The models outlined demonstrate that basic architectures can locally generate these oscillations, as well as capture other features of fast and slow gamma, including theta-phase preference and spontaneous transitions between gamma states. These models may extend to describe general dynamics in networks with diverse interneuron populations.

Indexed as

Gamma RhythmModels, NeurologicalAction PotentialsAnimalsCA1 Region, HippocampalHumansInterneuronsNeural Networks, ComputerNeural PathwaysPyramidal CellsRatsdynamical systemsgammahippocampusinterneuronoscillations

Identifiers

PMID27927782
PMCPMC5338627

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.