AUTHOR CONTRIBUTION: Q.S., D.M., and M.Z. designed and carried out the computational modeling experiments. N.O. and S.J.A. designed and carried out the behavioral and electrophysiological experiments. N.O. and S.J.A. carried out behavioral analyses and spike sorting to discriminate single-unit activity from electrophysiological recordings. D.M. carried out the analysis of network stability on electrophysiological recordings. Q.S. and M.Z. wrote the paper with assistance from N.O. and S.J.A.
Abstract:Critical-state dynamics in the brain have been shown to be an important feature for neural computation. However, links between criticality and network-level mechanisms underlying the formation of new memories are lacking. Here, we record from mice experiencing contextual fear conditioning and probe dynamics for signatures of criticality, finding criticality to be a natural state of the system. We subsequently measure the functional network stability (FuNS) of the recorded neurons and find that tracking changes in the network state before and after fear conditioning accurately predicts fear learning. We turn to modeling to determine the link between critical dynamics and FuNS. We control proximity to a balance between excitation and inhibition, a state we show to be synonymous with criticality, and observe a discrete set of local synaptic changes giving rise to global FuNS only near a critical state. Finally, new information has maximal consolidation potential only near criticality.
Author Summary:Much evidence points to the existence and corresponding implications of self-organized critical states in neuronal systems. Here, we expand on this work and show the importance of criticality on the network sensitivity to input and subsequent consolidation of new memories. Our in vivo studies suggest that critical states provide a necessary substrate for network-wide stabilization of functional interactions between neurons. Through modeling, we provide evidence that this socalled functional network stability is most sensitive only near a critical dynamical state. Further, we show that input has global effects on network dynamics only near criticality and, indeed, that new memories can be formed only at these states. Taken together, these results indicate criticalstate dynamics are vital for network sensitivity and consolidation potential.Contextual fear conditioning (CFC) is an optimal experimental paradigm to tackle these questions as it allows for rapid formation and consolidation of memory (i.e. after single-trial learning) 10 . Here, we first characterize hippocampal dynamics in mice subjected to CFC and show that: 1) the hippocampus operates in a near critical regime pre-and post-CFC traininga universal dynamical state indicating a dynamic phase transition, and 2) successful consolidation of fear memory leads to stabilization of network-wide functional representations.While the idea that the brain operates at or near dynamical critically is not new (see for example [11][12][13] and references therein) the functional bene...