ArticlePLoS computational biology2017
When do correlations increase with firing rates in recurrent networks?
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Dynamic Hippocampal CA2 Responses to Contextual Spatial Novelty.Frontiers in systems neuroscience · 2022Article
- Article
- Adaptive responses to neurodegenerative stress in glaucoma.Progress in retinal and eye research · 2021Review
- Differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation revealed by data-driven models.PLoS computational biology · 2021Article
- The Degree of Nesting between Spindles and Slow Oscillations Modulates Neural Synchrony.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Efficient calculation of heterogeneous non-equilibrium statistics in coupled firing-rate models.Journal of mathematical neuroscience · 2019Article
- Investigating the Correlation-Firing Rate Relationship in Heterogeneous Recurrent Networks.Journal of mathematical neuroscience · 2018Article
- Axogenic mechanism enhances retinal ganglion cell excitability during early progression in glaucoma.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.PLoS computational biology · 2018Article
- A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system.PLoS computational biology · 2017Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate-a relationship previously explained in feedforward networks driven by correlated input-emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix.
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