ArticleeLife2020
Patterned perturbation of inhibition can reveal the dynamical structure of neural processing.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 41 citations in OpenAlex.
- Inter-regional interactions uncouple within-region inhibition stabilization and paradoxical responses.bioRxiv : the preprint server for biology · 2026Article
- Characteristics and dynamical signatures of recurrent cortical circuits during context-dependent processing.bioRxiv : the preprint server for biology · 2026Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Principles of cortical interactions in modular recurrent networks.bioRxiv : the preprint server for biology · 2026Article
- Recurrent cortical networks encode natural sensory statistics via sequence filtering.bioRxiv : the preprint server for biology · 2025Article
- Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex.eLife · 2025Article
- Interactions between circuit architecture and plasticity in a closed-loop cerebellar system.eLife · 2024Article
- Identifying properties of pattern completion neurons in a computational model of the visual cortex.PLoS computational biology · 2023Article
- Distributing task-related neural activity across a cortical network through task-independent connections.Nature communications · 2023Article
- Article
- Intracortical microstimulation pulse waveform and frequency recruits distinct spatiotemporal patterns of cortical neuron and neuropil activation.Journal of neural engineering · 2022Article
- Modulation of the dynamical state in cortical network models.Current opinion in neurobiology · 2021Review
- Neocortical inhibitory interneuron subtypes are differentially attuned to synchrony- and rate-coded information.Communications biology · 2021Article
- Assessment of optogenetically-driven strategies for prosthetic restoration of cortical vision in large-scale neural simulation of V1.Scientific reports · 2021Article
- Training Spiking Neural Networks in the Strong Coupling Regime.Neural computation · 2021Article
- Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons.Neural plasticity · 2021Article
- CODECHECK: an Open Science initiative for the independent execution of computations underlying research articles during peer review to improve reproducibility.F1000Research · 2021Article
- Inhibitory stabilization and cortical computation.Nature reviews. Neuroscience · 2021Review
- Article
- Finding patterns in cortical responses.eLife · 2020Article
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2 authors at 1 institution in 1 country.
Funding
Abstract
Perturbation of neuronal activity is key to understanding the brain's functional properties, however, intervention studies typically perturb neurons in a nonspecific manner. Recent optogenetics techniques have enabled patterned perturbations, in which specific patterns of activity can be invoked in identified target neurons to reveal more specific cortical function. Here, we argue that patterned perturbation of neurons is in fact necessary to reveal the specific dynamics of inhibitory stabilization, emerging in cortical networks with strong excitatory and inhibitory functional subnetworks, as recently reported in mouse visual cortex. We propose a specific perturbative signature of these networks and investigate how this can be measured under different experimental conditions. Functionally, rapid spontaneous transitions between selective ensembles of neurons emerge in such networks, consistent with experimental results. Our study outlines the dynamical and functional properties of feature-specific inhibitory-stabilized networks, and suggests experimental protocols that can be used to detect them in the intact cortex.
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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.