Title
Critical Slowing Down Governs The Transition To Neuron Spiking
Abstract
Many complex systems have been found to exhibit critical transitions, or so-called tipping points, which are sudden changes to a qualitatively different system state. These changes can profoundly impact the functioning of a system ranging from controlled state switching to a catastrophic break-down; signals that predict critical transitions are therefore highly desirable. To this end, research efforts have focused on utilizing qualitative changes in markers related to a system's tendency to recover more slowly from a perturbation the closer it gets to the transition-a phenomenon called critical slowing down. The recently studied scaling of critical slowing down offers a refined path to understand critical transitions: to identify the transition mechanism and improve transition prediction using scaling laws.Here, we outline and apply this strategy for the first time in a real-world system by studying the transition to spiking in neurons of the mammalian cortex. The dynamical system approach has identified two robust mechanisms for the transition from subthreshold activity to spiking, saddle-node and Hopf bifurcation. Although theory provides precise predictions on signatures of critical slowing down near the bifurcation to spiking, quantitative experimental evidence has been lacking. Using whole-cell patch-clamp recordings from pyramidal neurons and fast-spiking interneurons, we show that 1) the transition to spiking dynamically corresponds to a critical transition exhibiting slowing down, 2) the scaling laws suggest a saddle-node bifurcation governing slowing down, and 3) these precise scaling laws can be used to predict the bifurcation point from a limited window of observation. To our knowledge this is the first report of scaling laws of critical slowing down in an experiment. They present a missing link for a broad class of neuroscience modeling and suggest improved estimation of tipping points by incorporating scaling laws of critical slowing down as a strategy applicable to other complex systems.
Year
DOI
Venue
2015
10.1371/journal.pcbi.1004097
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
membrane potential,bifurcation theory,action potentials,complex systems,autocorrelation
Statistical physics,Complex system,Biology,Bifurcation theory,Stochastic process,Artificial intelligence,Genetics,Scaling,Dynamical system,Hopf bifurcation,Bifurcation,Autocorrelation
Journal
Volume
Issue
ISSN
11
2
1553-734X
Citations 
PageRank 
References 
0
0.34
10
Authors
4
Name
Order
Citations
PageRank
Christian Meisel110.70
Andreas Klaus200.34
Christian Kuehn39012.21
Dietmar Plenz4284.33