ArticleFrontiers in computational neuroscience2014
Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Computing the effects of excitatory-inhibitory balance on neuronal input-output properties.PLoS computational biology · 2026Article
- Adaptive arousal regulation: Pharmacologically shifting the peak of the Yerkes-Dodson curve by catecholaminergic enhancement of arousal.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Article
- The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles.The European journal of neuroscience · 2021Review
- Computational Mechanisms for Perceptual Stability using Disparity and Motion Parallax.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Novel Functions of Feedback in Electrosensory Processing.Frontiers in integrative neuroscience · 2019Article
- Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition.PLoS computational biology · 2018Article
- The neural dynamics of sensory focus.Nature communications · 2015Article
- Subthreshold membrane responses underlying sparse spiking to natural vocal signals in auditory cortex.The European journal of neuroscience · 2015Article
- A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition.Frontiers in neurology · 2015Review
- Differential effects of excitatory and inhibitory heterogeneity on the gain and asynchronous state of sparse cortical networks.Frontiers in computational neuroscience · 2014Article
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5 authors.
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Abstract
The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry-also known as "open-loop feedback"-, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves) via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain.
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