Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics of cortical columns. While these protocols are challenging to perform experimentally, modelling can serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built a novel spiking network model of a cortical column, which incorporates pyramidal cells and three distinct interneuron types (PV, SST, and VIP cells, specified per lamina), as well as the dynamic and voltage-dependent properties of AMPA, GABA, and NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) and feedback (FB) stimuli arriving in the column have opposite effects, leading to net columnar excitation and inhibition respectively and revealing translaminar gain control via full-column inhibition by layer 6. We then perturb one cell group (i.e., a cell type in a specific layer) at a time and observe the effects on other cell groups under distinct network states: spontaneous, feedforward-driven, feedback-driven, and a combination of feedforward and feedback. Our findings reveal that when a given group is perturbed, the columnar response varies significantly based on its state, with strong sensory feedforward input decreasing columnar sensitivity to all perturbations and feedback input serving as modulator of intra columnar interactions. Given that activity changes within specific neuronal populations are difficult to predict a priori in experiments, our model may constitute a useful tool to predict outcomes of perturbations and assist in experimental design.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-04-23
DOI
https://doi.org/10.1371/journal.pcbi.1012036
Citations
4
Primary Topic
Neural dynamics and brain function
Type
article
Field-Weighted Citation Impact
5.72

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article

Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states

Jorge F. Mejías, Cyriel M. A. Pennartz, Giulia Moreni, Rares A Dorcioman
4 citations
PLoS Computational Biology
Neural dynamics and brain function
5.72
article

Cell-type-specific firing patterns in a V1 cortical column model depend on feedforward and feedback-driven states

Jorge F. Mejías, Cyriel M. A. Pennartz, Giulia Moreni, Rares A Dorcioman
article en
4 citations

Abstract

Stimulation of specific cell groups under different network regimes (e.g., spontaneous activity or sensory-evoked activity) can provide insights into the neural dynamics of cortical columns. While these protocols are challenging to perform experimentally, modelling can serve as a powerful tool for such explorations. Using detailed electrophysiological and anatomical data from mouse V1, we built a novel spiking network model of a cortical column, which incorporates pyramidal cells and three distinct interneuron types (PV, SST, and VIP cells, specified per lamina), as well as the dynamic and voltage-dependent properties of AMPA, GABA, and NMDA receptors. We first demonstrate that thalamocortical feedforward (FF) and feedback (FB) stimuli arriving in the column have opposite effects, leading to net columnar excitation and inhibition respectively and revealing translaminar gain control via full-column inhibition by layer 6. We then perturb one cell group (i.e., a cell type in a specific layer) at a time and observe the effects on other cell groups under distinct network states: spontaneous, feedforward-driven, feedback-driven, and a combination of feedforward and feedback. Our findings reveal that when a given group is perturbed, the columnar response varies significantly based on its state, with strong sensory feedforward input decreasing columnar sensitivity to all perturbations and feedback input serving as modulator of intra columnar interactions. Given that activity changes within specific neuronal populations are difficult to predict a priori in experiments, our model may constitute a useful tool to predict outcomes of perturbations and assist in experimental design.

PLoS Computational BiologyVol. 21(4)
Netherlands Institute for Neuroscience (NL), Amsterdam University of Applied Sciences (NL), University of Amsterdam (NL)
European Commission, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Horizon 2020 Framework Programme
Openalex Percentile: Top 4%
Neural dynamics and brain function
5.72
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