ArticleFrontiers in computational neuroscience2014
When do microcircuits produce beyond-pairwise correlations?
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- The quality and complexity of pairwise maximum entropy models for large cortical populations.PLoS computational biology · 2024Article
- Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons.Communications biology · 2023Article
- Autonomous emergence of connectivity assemblies via spike triplet interactions.PLoS computational biology · 2020Article
- Article
- Dynamical and Coupling Structure of Pulse-Coupled Networks in Maximum Entropy Analysis.Entropy (Basel, Switzerland) · 2019Article
- Generative models for network neuroscience: prospects and promise.Journal of the Royal Society, Interface · 2017Review
- Weak Higher-Order Interactions in Macroscopic Functional Networks of the Resting Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Signatures of criticality arise from random subsampling in simple population models.PLoS computational biology · 2017Article
- Stochastic Network Models in Neuroscience: A Festschrift for Jack Cowan. Introduction to the Special Issue.Journal of mathematical neuroscience · 2016Article
- How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?PLoS computational biology · 2016Article
- A Simple Mechanism for Beyond-Pairwise Correlations in Integrate-and-Fire Neurons.Journal of mathematical neuroscience · 2015Article
- Triplet correlations among similarly tuned cells impact population coding.Frontiers in computational neuroscience · 2015Article
- Correlated neuronal activity and its relationship to coding, dynamics and network architecture.Frontiers in computational neuroscience · 2014Article
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4 authors.
Funding
Abstract
Describing the collective activity of neural populations is a daunting task. Recent empirical studies in retina, however, suggest a vast simplification in how multi-neuron spiking occurs: the activity patterns of retinal ganglion cell (RGC) populations under some conditions are nearly completely captured by pairwise interactions among neurons. In other circumstances, higher-order statistics are required and appear to be shaped by input statistics and intrinsic circuit mechanisms. Here, we study the emergence of higher-order interactions in a model of the RGC circuit in which correlations are generated by common input. We quantify the impact of higher-order interactions by comparing the responses of mechanistic circuit models vs. "null" descriptions in which all higher-than-pairwise correlations have been accounted for by lower order statistics; these are known as pairwise maximum entropy (PME) models. We find that over a broad range of stimuli, output spiking patterns are surprisingly well captured by the pairwise model. To understand this finding, we study an analytically tractable simplification of the RGC model. We find that in the simplified model, bimodal input signals produce larger deviations from pairwise predictions than unimodal inputs. The characteristic light filtering properties of the upstream RGC circuitry suppress bimodality in light stimuli, thus removing a powerful source of higher-order interactions. This provides a novel explanation for the surprising empirical success of pairwise models.
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