ArticleJournal of computational neuroscience2011
Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons.
Article in Journal of computational neuroscience, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Critical dynamics predicts cognitive performance and provides a common framework for heterogeneous mechanisms impacting cognition.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Lateral inhibition in V1 controls neural and perceptual contrast sensitivity.Nature neuroscience · 2025Article
- Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Decomposing Neural Circuit Function into Information Processing Primitives.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load.PLoS computational biology · 2023Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- On the Validity of Neural Mass Models.Frontiers in computational neuroscience · 2020Article
- Modeling mesoscopic cortical dynamics using a mean-field model of conductance-based networks of adaptive exponential integrate-and-fire neurons.Journal of computational neuroscience · 2018Article
- Dynamic Communications Between GABAFrontiers in cellular neuroscience · 2018Article
- VIOLA-A Multi-Purpose and Web-Based Visualization Tool for Neuronal-Network Simulation Output.Frontiers in neuroinformatics · 2018Article
- Integration of Continuous-Time Dynamics in a Spiking Neural Network Simulator.Frontiers in neuroinformatics · 2017Article
- The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding.Frontiers in computational neuroscience · 2017Article
- Role of input correlations in shaping the variability and noise correlations of evoked activity in the neocortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015Article
- Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo.Nature communications · 2014Article
- The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave.Nature communications · 2014Article
- Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model.PloS one · 2014Article
- Distribution of orientation selectivity in recurrent networks of spiking neurons with different random topologies.PloS one · 2014Article
- Dynamic stability of sequential stimulus representations in adapting neuronal networks.Frontiers in computational neuroscience · 2014Article
- Mean-field analysis of orientation selectivity in inhibition-dominated networks of spiking neurons.SpringerPlus · 2014Article
- Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model.Journal of computational neuroscience · 2013Article
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4 authors.
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Abstract
The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparsely-connected networks of conductance-based integrate-and-fire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the "macroscopic" properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussian-distributed local connectivity. We systematically computed the distance-dependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, provided the excitatory-inhibitory balance is the same. In particular, the same correlation structure holds for different connectivity profiles. In addition, we examined the response of such networks to external input, and found that the correlation landscape can be modulated by the mean level of synchrony imposed by the external drive. This modulation was found again to be independent of the external connectivity profile. We conclude that first and second-order "mean-field" statistics of such networks do not depend on the details of the connectivity at a microscopic scale. This study is an encouraging step toward a mean-field description of topological neuronal networks.
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