Two types of criticality in the brain

Abstract

Neural networks with equal excitatory and inhibitory feedback show high computational performance. They operate close to a critical point characterized by the joint activation of large populations of neurons. Yet, in macaque motor cortex we observe very different dynamics with weak fluctuations on the population level. This suggests that motor cortex operates in a sub-optimal regime. Here we show the opposite: the large dispersion of correlations across neurons is a signature of a rich dynamical repertoire, hidden from macroscopic brain signals, but essential for high performance in such concepts as reservoir computing. Our findings suggest a refinement of the view on criticality in neural systems: network topology and heterogeneity endow the brain with two complementary substrates for critical dynamics of largely different complexities.

References in corpus (17)

• Are biological systems poised at criticality?
• Quantum Field Theory in the Large N Limit: a review
• Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex
• Spectrum of Controlling and Observing Complex Networks
• Searching for collective behavior in a network of real neurons
• Decorrelation of neural-network activity by inhibitory feedback
• Predicting criticality and dynamic range in complex networks: effects of topology
• Impact of network structure and cellular response on spike time correlations
• Stimuli reduce the dimensionality of cortical activity
• Universal properties of correlation transfer in integrate-and-fire neurons
• Inferring collective dynamical states from widely unobserved systems
• Topological and Dynamical Complexity of Random Neural Networks
• Dynamical criticality in the collective activity of a population of retinal neurons
• Path Integral Approach to Random Neural Networks
• A unified view on weakly correlated recurrent networks
• Predicting how and when hidden neurons skew measured synaptic interactions
• Network Inference with Hidden Units

Cited by in corpus (22)

• Dimension of activity in random neural networks
• Does the brain behave like a (complex) network? I. Dynamics
• Criticality in spreading processes without time-scale separation and the critical brain hypothesis
• Assessing criticality in pre-seizure single-neuron activity of human epileptic cortex
• Tackling the subsampling problem to infer collective properties from limited data
• Tracking the distance to criticality in systems with unknown noise
• Self-consistent formulations for stochastic nonlinear neuronal dynamics
• Suppression of chaos in a partially driven recurrent neural network
• Prominent characteristics of recurrent neuronal networks are robust against low synaptic weight resolution
• A Microscopic Theory of Intrinsic Timescales in Spiking Neural Networks
• Dynamically selected steady states and criticality in non-reciprocal networks
• Nonreciprocal Spin-Glass Transition and Aging
• Effect of Synaptic Heterogeneity on Neuronal Coordination
• Capacity of the covariance perceptron
• Energy-information trade-off makes the cortical critical power law the optimal coding
• Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles
• Criticality at work: scaling in the mouse cortex enhances performance
• Neuronal correlations shape the scaling behavior of memory capacity and nonlinear computational capability of reservoir recurrent neural networks
• Phase transitions in in vivo or in vitro populations of spiking neurons belong to different universality classes
• Hierarchy of chaotic dynamics in random modular networks
• How random connectivity shapes the fluctuating dynamics of finite-size neural populations
• Statistics of correlations in nonlinear recurrent neural networks