Coarse--graining, fixed points, and scaling in a large population of neurons

Abstract

We develop a phenomenological coarse--graining procedure for activity in a large network of neurons, and apply this to recordings from a population of 1000+ cells in the hippocampus. Distributions of coarse--grained variables seem to approach a fixed non--Gaussian form, and we see evidence of scaling in both static and dynamic quantities. These results suggest that the collective behavior of the network is described by a non--trivial fixed point.

References in corpus (9)

• Are biological systems poised at criticality?
• Whole-brain calcium imaging with cellular resolution in freely behaving C. elegans
• Power-law statistics and universal scaling in the absence of criticality
• Social interactions dominate speed control in driving natural flocks toward criticality
• Scaling in ordered and critical random Boolean networks
• Collective behavior of place and non-place neurons in the hippocampal network
• Morphogenesis at criticality?
• Dynamic scaling in natural swarms
• PCA meets RG

Cited by in corpus (27)

• Self-organization toward criticality by synaptic plasticity
• Laplacian Renormalization Group for heterogeneous networks
• Hybrid-type synchronization transitions: where marginal coherence, scale-free avalanches, and bistability live together
• Latent dynamical variables produce signatures of spatiotemporal criticality in large biological systems
• Testing the critical brain hypothesis using a phenomelogical renormalization group
• Disentangling the critical signatures of neural activity
• Tackling the subsampling problem to infer collective properties from limited data
• Bayesian Renormalization
• Field theoretical approach for signal detection in nearly continuous positive spectra II: Tensorial data
• Network Renormalization
• Signal detection in nearly continuous spectra and symmetry breaking
• Field theoretical approach for signal detection in nearly continuous positive spectra I: Matricial data
• Statistical properties of large data sets with linear latent features
• Jensen's force and the statistical mechanics of cortical asynchronous states
• Inferring couplings in networks across order-disorder phase transitions
• Discovering the mesoscale for chains of conflict
• Optimal encoding in stochastic latent-variable Models
• Ambitions for theory in the physics of life
• Criticality at work: scaling in the mouse cortex enhances performance
• Extrinsic vs Intrinsic Criticality in Systems with Many Components
• Discovering sparse control strategies in C. elegans
• Phase transitions in in vivo or in vitro populations of spiking neurons belong to different universality classes
• Functional renormalization group for signal detection and stochastic ergodicity breaking
• Generative random latent features models and statistics of natural images
• Functional Renormalization Group Approach for Signal Detection
• Renormalization group for spectral collapse in random matrices with power-law variance profiles
• Gaussian Information Bottleneck and the Non-Perturbative Renormalization Group