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 (4)

• Emergent complex neural dynamics
• Identification of direct residue contacts in protein-protein interaction by message passing
• Dynamical synapses causing self-organized criticality in neural networks
• Prediction of spatio-temporal patterns of neural activity from pairwise correlations

Cited by in corpus (8)

• A tractable method for describing complex couplings between neurons and population rate
• Maximum Caliber Inference and the Stochastic Ising Model
• Inferring couplings in networks across order-disorder phase transitions
• Ubiquitous proximity to a critical state for collective neural activity in the CA1 region of freely moving mice
• 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