Date of Award

8-19-2024

Author's School

Graduate School of Arts and Sciences

Author's Department

Biology & Biomedical Sciences (Neurosciences)

Degree Name

Doctor of Philosophy (PhD)

Degree Type

Dissertation

Abstract

Computation within the brain necessitates consistency and robustness, as no complex system can function effectively without these fundamental attributes. However, maintaining this robustness requires active efforts due to the inevitable perturbations (Turrigiano & Nelson, 2004). To unlock the unifying principle of how brain works, it is essential to identify an optimal set point that the system strives to maintain and restore, ensuring stability and functionality. Our lab recently demonstrated that cortical dynamics are homeostatically organized around criticality (Ma et al., 2019), a computational regime that optimizes information processing, such as information capacity and dynamic range (Shew & Plenz, 2013). This finding indicates that criticality serves as an independent homeostatic set point of brain dynamics, which intrinsically reflects brain function. Our modeling and many previous works revealed that Hebbian plasticity could disrupt this critical regime, raising the question of how diverse circuits actively maintain this optimal set point in the face of experience-dependent plasticity (Ma et al., 2019; Meisel, Klaus, et al., 2017). Therefore, this dissertation aims to investigate how brain maintains criticality. Sleep is vitally important for promoting stable brain function, but how sleep contributes to robust and efficient neural computation at the network level remains unknown. Based on the philosophy that “sleep is the price that brain pays for plasticity” and previous findings that sleep compensates for multi-dimensional plastic changes during wake to support complex cognition (Meisel, Klaus, et al., 2017; Tononi & Cirelli, 2014), I propose that the recovery of criticality, the homeostatic set point of circuit dynamics, in plastic networks is one of the core restorative functions of sleep. With in vivo recording techniques, we aim to characterize the impact of waking experience and sleep on critical dynamics. Our central hypothesis is that waking experience progressively perturbs the critical regime, while sleep functions to restore criticality in plastic neural networks. To determine the impact of sleep and wake on criticality, we implanted micro-electrode arrays into the primary visual cortex of freely-behaving rats and tracked the activity of ensembles of single units for over 10 days. With this high-density recording, we could extract and assess neuronal avalanches, defined as the propagation of population activities, to quantitatively determine how close to criticality the network is operating. We examined criticality as a function of brain states, behavior, environment, and time of the day in both baseline and extended waking conditions. First, we found that statistical properties of neural avalanches vary in different behavioral states and environmental conditions, which could be understood in the framework of quasi-criticality (Chapter 2). Second, our data revealed that in the context of free behavior, time spent awake positively correlates with the network’s distance to criticality, and time spent asleep counteracts this effect. Sleep deprivation causally disrupted critical dynamics. These results demonstrated that sleep functions to homeostatically restore the critical regime, which is progressively undermined during waking experience. Importantly, the perturbation of criticality during waking experiences is context-dependent (Chapter 3). Third, the extent to which neural dynamics deviate from criticality predicts the amount of future sleep more accurately than Slow-wave activity (SWA), behavioral history, and other neural measures, further suggesting that maintenance of criticality is a core purpose of sleep (Chapter 4). Our results support the hypothesis that the recovery of criticality is a network homeostatic mechanism consistent with the restorative function of sleep. This dissertation establishes a theory-driven model describing how sleep and wake modify the computational regime of the neural networks, advances the understanding of how brain maintains robust and efficient computation over long timescales, and offers a novel, system-level explanation of the cognitive benefit of sleep.

Language

English (en)

Chair and Committee

Keith Hengen

Committee Members

Erik Herzog; Geoffrey Goodhill; Keith Hengen; Ralf Wessel; Timothy Holy

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