ArticleNetwork (Bristol, England)
Gamma frequency feedback inhibition accounts for key aspects of orientation selectivity in V1.
Article in Network (Bristol, England). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Structured interconnectivity optimizes neural geometry for balancing specificity and generalization in object recognition.Communications biology · 2025Article
- Regulation of gamma-frequency oscillation by feedforward inhibition: A computational modeling study.Hippocampus · 2019Article
- Glutamatergic synapses are structurally and biochemically complex because of multiple plasticity processes: long-term potentiation, long-term depression, short-term potentiation and scaling.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2017Review
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1 author.
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Abstract
There is now strong evidence that gamma frequency oscillations occur during the engagement of cortical regions. These oscillations involve gamma frequency feedback inhibition. Thus, understanding the properties of this form of inhibition is critical to understanding how excitation and inhibition interact to determine which cells fire and, more generally, how cortex performs computations. In previous work, we argued that gamma frequency inhibition performs a type of winner-take-all computation that obeys simple rules: 1) cells fire if their excitation is within E% of the cell with maximum excitation; 2) E%max is determined by the delay of feedback inhibition and the membrane time constant. This framework was previously applied to the best-studied cortical computation, orientation selectivity of cells in V1. Measurements show that orientation tuning is insensitive to illumination contrast. We showed that this finding can be simply explained by the E%max model. Recently, a new property of orientation selectivity has been discovered: orientation tuning varies with the phase of the gamma oscillation. Here, we show that this too can be simply explained by the E%max model. These successes suggest that simple rules underlie the selection of which cells fire in cortical networks.
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