Inhibitory stabilization of the cortical network underlies visual surround suppression

Ozeki, Hirofumi; Finn, Ian M.; Schaffer, Evan; Miller, Kenneth D.; Ferster, David

doi:10.1016/j.neures.2009.09.1125

Cited by 52 publications

(122 citation statements)

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“…Second, we observe smaller MRF in Layer 2/3 than in Layer 4 (see above data) in line with observations in Jones et al (2000). Finally, we find that both excitatory and inhibitory conductances are size tuned at both low and high contrast and in both simulated cortical layers ( Figure 9H), in line with experimental evidence (Anderson et al 2001;Ozeki et al 2009).…”

Section: Size Tuningsupporting

confidence: 89%

A comprehensive data-driven model of cat primary visual cortex

Antolík

Monier

Frégnac

et al. 2018

Preprint

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Knowledge integration based on the relationship between structure and function of the neural substrate is one of the main targets of neuroinformatics and data-driven computational modeling. However, the multiplicity of data sources, the diversity of benchmarks, the mixing of observables of different natures, and the necessity of a long-term, systematic approach make such a task challenging. Here we present a first snapshot of a long-term integrative modeling program designed to address this issue: a comprehensive spiking model of cat primary visual cortex satisfying an unprecedented range of anatomical, statistical and functional constraints under a wide range of visual input statistics. In the presence of physiological levels of tonic stochastic bombardment by spontaneous thalamic activity, the modeled cortical reverberations self-generate a sparse asynchronous ongoing activity that quantitatively matches a range of experimentally measured statistics. When integrating feed-forward drive elicited by a high diversity of visual contexts, the simulated network produces a realistic, quantitatively accurate interplay between visually evoked excitatory and inhibitory conductances; contrast-invariant orientation-tuning width; center surround interactions; and stimulus-dependent changes in the precision of the neural code.This integrative model offers numerous insights into how the studied properties interact, contributing to a better understanding of visual cortical dynamics. It provides a basis for future development towards a comprehensive model of low-level perception. Significance statementComputational modeling can integrate fragments of understanding generated by experimental neuroscience. However, most previous models considered only a few features of neural computation at a time, leading to either poorly constrained models with many parameters, or lack of expressiveness in over-simplified models. A solution is to commit to detailed models, but constrain them with a broad range of anatomical and functional data. This requires a long-term systematic approach. Here we present a first snapshot of such an integrative program: a large-scale spiking model of V1, that is constrained by an unprecedented range of anatomical and functional features. Together with the associated modeling infrastructure, this study lays the groundwork for a broad integrative modeling program seeking an in-depth understanding of vision. Keywordsvisual cortex, cortical microcircuit, large-scale models, comprehensive modeling, layered network 2 resting-conductance regime emerges in the model. Under visual stimulation, the same model exhibits diversity of the interplay between evoked excitation and inhibition; stimulus-locked subthreshold variability; contrast-invariant orientation tuning; size tuning; stimulus-dependent firing precision; and a realistic distribution of Simple/Complex receptive fields.This study advances our ability to capture the function of V1 within a single model, and provides insights into how the studied properties interac...

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Section: Size Tuningsupporting

confidence: 89%

A comprehensive data-driven model of cat primary visual cortex

Antolík

Monier

Frégnac

et al. 2018

Preprint

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“…All layers induced gamma rhythms in cells in their own layer that were characterized by a relatively low E/I balance -that is one tilted more strongly towards inhibition and net suppression of activity. This powerful recurrent inhibition is likely to be crucial for stabilizing feedback excitation (Tsodyks et al 1997;Pinto et al 2003;Ozeki et al 2009) that leads to gamma band activity (Jadi & Sejnowski, 2014a,b) by providing the basis for the cortex to enter an inhibitory stabilized network (Ozeki et al 2009). This inhibition would then contribute to fundamental cortical computations such a normalization and contextual modulation (Carandini et al 1997;Ozeki et al 2009;Nassi et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Layer‐specific excitation/inhibition balances during neuronal synchronization in the visual cortex

Adesnik

2018

The Journal of Physiology

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Rhythmic activity can synchronize neural ensembles within and across cortical layers. While gamma band rhythmicity has been observed in all layers, the laminar sources and functional impacts of neuronal synchronization in the cortex remain incompletely understood. Here, layer-specific optogenetic stimulation demonstrates that populations of excitatory neurons in any cortical layer of the mouse's primary visual cortex are sufficient to powerfully entrain neuronal oscillations in the gamma band. Within each layer, inhibition balances excitation and keeps activity in check. Across layers, translaminar output overcomes inhibition and drives downstream firing. These data establish that rhythm-generating circuits exist in all principle layers of the cortex, but provide layer-specific balances of excitation and inhibition that may dynamically shape the flow of information through cortical circuits. These data might help explain how excitation/inhibition (E/I) balances across cortical layers shape information processing, and shed light on the diverse nature and functional impacts of cortical gamma rhythms.

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“…In this article, we used neural fields to model the dispersion of axonal connections and describe the effect of surround suppression on gamma responses, see also [Pinotsis et al, , 2014. Finally, our source model comprises four populations whose connectivity is similar to recent theoretical and experimental work that focuses on the origins of visually induced gamma peak [Jadi and Sejnowski, 2014b;Ozeki et al, 2009].…”

Section: Discussionmentioning

confidence: 99%

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

Pinotsis

Perry

Singh

et al. 2016

Human Brain Mapping

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This article describes the first application of a generic (empirical) Bayesian analysis of between‐subject effects in the dynamic causal modeling (DCM) of electrophysiological (MEG) data. It shows that (i) non‐invasive (MEG) data can be used to characterize subject‐specific differences in cortical microcircuitry and (ii) presents a validation of DCM with neural fields that exploits intersubject variability in gamma oscillations. We find that intersubject variability in visually induced gamma responses reflects changes in the excitation‐inhibition balance in a canonical cortical circuit. Crucially, this variability can be explained by subject‐specific differences in intrinsic connections to and from inhibitory interneurons that form a pyramidal‐interneuron gamma network. Our approach uses Bayesian model reduction to evaluate the evidence for (large sets of) nested models—and optimize the corresponding connectivity estimates at the within and between‐subject level. We also consider Bayesian cross‐validation to obtain predictive estimates for gamma‐response phenotypes, using a leave‐one‐out procedure. Hum Brain Mapp 37:4597–4614, 2016. © The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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Inhibitory stabilization of the cortical network underlies visual surround suppression

Cited by 52 publications

References 0 publications

A comprehensive data-driven model of cat primary visual cortex

A comprehensive data-driven model of cat primary visual cortex

Layer‐specific excitation/inhibition balances during neuronal synchronization in the visual cortex

Intersubject variability and induced gamma in the visual cortex: DCM with empirical Bayes and neural fields

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