Dynamical criticality in the collective activity of a population of retinal neurons

Abstract

Recent experimental results based on multi-electrode and imaging techniques have reinvigorated the idea that large neural networks operate near a critical point, between order and disorder. However, evidence for criticality has relied on the definition of arbitrary order parameters, or on models that do not address the dynamical nature of network activity. Here we introduce a novel approach to assess criticality that overcomes these limitations, while encompassing and generalizing previous criteria. We find a simple model to describe the global activity of large populations of ganglion cells in the rat retina, and show that their statistics are poised near a critical point. Taking into account the temporal dynamics of the activity greatly enhances the evidence for criticality, revealing it where previous methods would not. The approach is general and could be used in other biological networks.

References in corpus (15)

• Weak pairwise correlations imply strongly correlated network states in a neural population
• Emergent complex neural dynamics
• Identification of direct residue contacts in protein-protein interaction by message passing
• Are biological systems poised at criticality?
• Dynamical synapses causing self-organized criticality in neural networks
• Essential nonlinearities in hearing
• Maximum entropy models for antibody diversity
• Social interactions dominate speed control in driving natural flocks toward criticality
• Prediction of spatio-temporal patterns of neural activity from pairwise correlations
• Morphogenesis at criticality?
• The simplest maximum entropy model for collective behavior in a neural network
• Dynamical and Statistical Criticality in a Model of Neural Tissue
• Thermodynamics of natural images
• Dynamical maximum entropy approach to flocking
• Gibbs distribution analysis of temporal correlations structure in retina ganglion cells

Cited by in corpus (35)

• Colloquium: Criticality and dynamical scaling in living systems
• Inverse statistical problems: from the inverse Ising problem to data science
• Collective behavior of place and non-place neurons in the hippocampal network
• Distributions of covariances as a window into the operational regime of neuronal networks
• Two types of criticality in the brain
• Determination of the edge of criticality in echo state networks through Fisher information maximization
• What do we mean by the dimensionality of behavior?
• Perspectives on theory at the interface of physics and biology
• Signatures of criticality arise in simple neural population models with correlations
• Subsampled directed-percolation models explain scaling relations experimentally observed in the brain
• Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems
• A unifying framework for mean-field theories of asymmetric kinetic Ising systems
• Constraint satisfaction mechanisms for marginal stability and criticality in large ecosystems
• Learning Maximum Entropy Models from finite size datasets: a fast Data-Driven algorithm allows sampling from the posterior distribution
• A tractable method for describing complex couplings between neurons and population rate
• Balancing selfishness and norm conformity can explain human behavior in large-scale Prisoner's Dilemma games and can poise human groups near criticality
• Blindfold learning of an accurate neural metric
• Maximum Caliber Inference and the Stochastic Ising Model
• Ensemble Inhibition and Excitation in the Human Cortex: an Ising Model Analysis with Uncertainties
• Pairwise maximum-entropy models and their Glauber dynamics: bimodality, bistability, non-ergodicity problems, and their elimination via inhibition
• Stationary properties of maximum entropy random walks
• The Structured `Low Temperature' Phase of the Retinal Population Code
• Modeling the correlated activity of neural populations: A review
• Signatures of brain criticality unveiled by maximum entropy analysis across cortical states
• Inferring a network from dynamical signals at its nodes
• Inferring couplings in networks across order-disorder phase transitions
• Resummed mean-field inference for strongly coupled data
• Ubiquitous proximity to a critical state for collective neural activity in the CA1 region of freely moving mice
• Semiparametric energy-based probabilistic models
• Extrinsic vs Intrinsic Criticality in Systems with Many Components
• Inference in neural networks using conditional mean-field methods
• Noise-Robust Modes of the Retinal Population Code have the Geometry of "Ridges" and Correspond with Neuronal Communities
• Mapping Inter-City Trade Networks to Maximum Entropy Models using Electronic Invoice Data
• Information Entropy Production of Spatio-Temporal Maximum Entropy Distributions
• A General Approach to Coding in Early Olfactory and Visual Neural Populations