Mitochondria: new players in homeostatic regulation of firing rate set points
Authors: Antonella Ruggiero, Maxim Katsenelson, Inna Slutsky
Journal: Trends in Neurosciences (2021)

Firing rate distributions and mean firing rate (MFR) present homeostatically regulated variables in central neural circuits.Mitochondrial Ca2+ buffering is involved in the regulation of the main homeostatic modules underlying firing rate stabilization: set points, sensors, and effectors.The core homeostatic machinery can be identified by a dual-challenge approach. Using this framework, the mitochondrial dihydroorotate dehydrogenase (DHODH) enzyme has been uncovered as a regulator of MFR set points in hippocampal networks.MFRs and firing rate distributions are homeostatically regulated by sleep in specific neural circuits. As products of DHODH enzymatic activity are inhibited by sleep, DHODH inhibition may mediate a homeostatic decrease of MFRs during sleep.Mitochondrial dysfunctions constitute a common hallmark of distinct brain disorders due to a central role of mitochondria in homeostatic firing rate regulation. Neural circuit functions are stabilized by homeostatic processes at long timescales in response to changes in behavioral states, experience, and learning. However, it remains unclear which specific physiological variables are being stabilized and which cellular or neural network components compose the homeostatic machinery. At this point, most evidence suggests that the distribution of firing rates among neurons in a neuronal circuit is the key variable that is maintained around a set-point value in a process called 'firing rate homeostasis.' Here, we review recent findings that implicate mitochondria as central players in mediating firing rate homeostasis. While mitochondria are known to regulate neuronal variables such as synaptic vesicle release or intracellular calcium concentration, the mitochondrial signaling pathways that are essential for firing rate homeostasis remain largely unknown. We used basic concepts of control theory to build a framework for classifying possible components of the homeostatic machinery that stabilizes firing rate, and we particularly emphasize the potential role of sleep and wakefulness in this homeostatic process. This framework may facilitate the identification of new homeostatic pathways whose malfunctions drive instability of neural circuits in distinct brain disorders. Neural circuit functions are stabilized by homeostatic processes at long timescales in response to changes in behavioral states, experience, and learning. However, it remains unclear which specific physiological variables are being stabilized and which cellular or neural network components compose the homeostatic machinery. At this point, most evidence suggests that the distribution of firing rates among neurons in a neuronal circuit is the key variable that is maintained around a set-point value in a process called 'firing rate homeostasis.' Here, we review recent findings that implicate mitochondria as central players in mediating firing rate homeostasis. While mitochondria are known to regulate neuronal variables such as synaptic vesicle release or intracellular calcium concentration, the mitochondrial signaling pathways that are essential for firing rate homeostasis remain largely unknown. We used basic concepts of control theory to build a framework for classifying possible components of the homeostatic machinery that stabilizes firing rate, and we particularly emphasize the potential role of sleep and wakefulness in this homeostatic process. This framework may facilitate the identification of new homeostatic pathways whose malfunctions drive instability of neural circuits in distinct brain disorders. The concept of homeostasis, based on the classical works of Claude Bernard, Walter Cannon, and James Hardy, refers to the mechanisms that maintain physiological variables within a dynamic range around a 'set point' [1.Bernard C. Lessons on the phenomena of life common to animals and vegetables. Second lecture, the three forms of life.in: Langley L.L. Homeostasis: Origins of the concept. Hutchinson & Ross, Inc, Dowden1973, originally published 1870: 129-151Google Scholar, 2.Cannon W.B. Organization for physiological homeostasis.Physiol. Rev. 1929; 9: 399-431Crossref Google Scholar, 3.Hardy J.D. Control of heat loss and heat production in physiologic temperature regulation.Harvey Lect. 1953; 49: 242-270PubMed Google Scholar]. In the context of neural circuits, homeostatic negative feedbacks enable stable activity of neural networks over long timescales, despite the highly dynamic and heterogeneous nature of individual synapses and neurons. Without such homeostatic feedback, the circuit's function may be destabilized by Hebbian-like synaptic plasticity underlying the cellular basis of learning and memory [4.Abbott L.F. Nelson S.B. Synaptic plasticity: taming the beast.Nat. Neurosci. 2000; 3: 1178-1183Crossref PubMed Scopus (1291) Google Scholar,5.Turrigiano G.G. The dialectic of Hebb and homeostasis.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2017; 37220160258Crossref PubMed Scopus (83) Google Scholar]. This plasticity-stability problem has been introduced and elegantly reviewed in earlier insightful papers [6.Turrigiano G.G. Nelson S.B. Homeostatic plasticity in the developing nervous system.Nat. Rev. Neurosci. 2004; 5: 97-107Crossref PubMed Google Scholar, 7.Davis G.W. Homeostatic control of neural activity: from phenomenology to molecular design.Annu. Rev. Neurosci. 2006; 29: 307-323Crossref PubMed Scopus (393) Google Scholar, 8.Marder E. Goaillard J.M. Variability, compensation and homeostasis in neuron and network function.Nat. Rev. Neurosci. 2006; 7: 563-574Crossref PubMed Scopus (696) Google Scholar], but many key questions remain unanswered. In particular, what are the components of the core homeostatic machinery at the subcellular and neural network levels, and what variable(s) do they regulate to prevent aberrant long-term changes in neural network activity? The function of many cellular variables such as synaptic weights, ion channels, neurotransmitter release, and receptor expression are dynamic under normal conditions, and scientists are challenged to dissect which of these dynamics are homeostatic in nature. Application of engineering control theory [7.Davis G.W. Homeostatic control of neural activity: from phenomenology to molecular design.Annu. Rev. Neurosci. 2006; 29: 307-323Crossref PubMed Scopus (393) Google Scholar] can be used to navigate this issue, based on the following principal characteristics: (i) a set point that the system must return to following a perturbation, which defines the output of the homeostatic machinery; (ii) sensors that detect deviation from that set point; and (iii) homeostatic effectors that precisely retarget some regulated variable to that set point via negative feedback (Figure 1A ). Accordingly, when the regulated variable returns to a value close to the set point, all effectors are at minimal or basal levels of activity. Here, we focus on the mechanisms that regulate the establishment of activity set points in neuronal networks with particular emphasis on mitochondria. How are set points encoded by neurons and their networks? What are the sensors that detect changes in set point values? How tightly regulated must these values be to prevent neural circuit dysfunction and disease? What mechanisms are in place to encode behavioral state-dependent set points? Addressing these questions is critical for delineating the mechanisms underlying the stability of neural networks. The operation of a neuronal circuit depends on the interaction between the intrinsic properties of the individual neurons and the synaptic interactions that connect them into functional ensembles. Despite a large variability in these synaptic and intrinsic parameters, the mean firing rate (MFR) of a neuronal population during ongoing spontaneous activity is typically preserved at a specific set-point value. For instance, the MFR of a neuronal population gradually renormalizes despite the constant presence of a pharmacological, genetic, or experience-dependent perturbation that initially caused a rapid change in MFR (Figure 1B,C). This process dubbed 'firing rate homeostasis,' occurs robustly in cultured neural networks ex vivo [9.Turrigiano G.G. et al.Activity-dependent scaling of quantal amplitude in neocortical neurons.Nature. 1998; 391: 892-896Crossref PubMed Scopus (1505) Google Scholar, 10.Burrone J. et al.Multiple forms of synaptic plasticity triggered by selective suppression of activity in individual neurons.Nature. 2002; 420: 414-418Crossref PubMed Scopus (355) Google Scholar, 11.Slomowitz E. et al.Interplay between population firing stability and single neuron dynamics in hippocampal networks.Elife. 2015; 4e04378Crossref Scopus (48) Google Scholar, 12.Vertkin I. et al.GABAB receptor deficiency causes failure of neuronal homeostasis in hippocampal networks.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: E3291-E3299Crossref PubMed Scopus (24) Google Scholar, 13.Styr B. et al.Mitochondrial regulation of the hippocampal firing rate set point and seizure susceptibility.Neuron. 2019; 102: 1009-1024.e8Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar, 14.Tyssowski K.M. et al.Firing rate homeostasis can occur in the absence of neuronal activity-regulated transcription.J. Neurosci. 2019; 39: 9885-9899Crossref PubMed Scopus (0) Google Scholar], and has been documented in vivo as well, such as in the rodent primary visual cortex (V1) [15.Hengen K.B. et al.Firing rate homeostasis in visual cortex of freely behaving rodents.Neuron. 2013; 80: 335-342Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar, 16.Keck T. et al.Synaptic scaling and homeostatic plasticity in the mouse visual cortex in vivo.Neuron. 2013; 80: 327-334Abstract Full Text Full Text PDF PubMed Scopus (158) Google Scholar, 17.Barnes S.J. et al.Subnetwork-specific homeostatic plasticity in mouse visual cortex in vivo.Neuron. 2015; 86: 1290-1303Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. On the basis of these results, MFR can be classified as a physiological variable that undergoes homeostatic set-point regulation. How does the network's behavior relate to properties of its single components? Multiple spatial set points may exist, and the specific set point may undergo regulation by homeostatic mechanisms implemented at these various spatial scales [18.Keck T. et al.Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2017; 37220160158Crossref PubMed Scopus (79) Google Scholar,19.Turrigiano G. Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function.Cold Spring Harb. Perspect. Biol. 2012; 4a005736Crossref PubMed Scopus (519) Google Scholar]. In some cases, firing rate homeostasis can be achieved by cell-autonomous mechanisms at the level of single neurons, as in the case of the crustacean stomatogastric ganglion [8.Marder E. Goaillard J.M. Variability, compensation and homeostasis in neuron and network function.Nat. Rev. Neurosci. 2006; 7: 563-574Crossref PubMed Scopus (696) Google Scholar] and monocular V1 of rats [20.Hengen K.B. et al.Neuronal firing rate homeostasis is inhibited by sleep and promoted by wake.Cell. 2016; 165: 180-191Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar]. Moreover, local homeostatic mechanisms may operate at the level of a dendritic branch to maintain total synaptic strength [21.Branco T. et al.Local dendritic activity sets release probability at hippocampal synapses.Neuron. 2008; 59: 475-485Abstract Full Text Full Text PDF PubMed Scopus (171) Google Scholar,22.Laviv T. et al.Basal GABA Regulates GABABR conformation and release probability at single hippocampal synapses.Neuron. 2010; 67: 253-267Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar] or excitation-to-inhibition ratio [23.Liu G. Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites.Nat. Neurosci. 2004; 7: 373-379Crossref PubMed Scopus (213) Google Scholar]. Given a high level of instability of a single neuron firing at long timescales in some neural circuits, such as the hippocampal one [24.Ziv Y. et al.Long-term dynamics of CA1 hippocampal place codes.Nat. Neurosci. 2013; 16: 264-266Crossref PubMed Scopus (451) Google Scholar], additional sensors and effectors may operate globally to stabilize firing at a population level. Indeed, in cultured neural networks, MFR recovery at the population level is more tightly regulated than at the level of single neurons [11.Slomowitz E. et al.Interplay between population firing stability and single neuron dynamics in hippocampal networks.Elife. 2015; 4e04378Crossref Scopus (48) Google Scholar,25.Maffei A. Fontanini A. Network homeostasis: a matter of coordination.Curr. Opin. Neurobiol. 2009; 19: 168-173Crossref PubMed Scopus (71) Google Scholar]. Notably, instability of individual neurons grown in culture is largely intrinsic, as it takes place in a highly controlled environment, regardless of changes in experience, behavioral states, and interactions with higher-order supervising circuits [11.Slomowitz E. et al.Interplay between population firing stability and single neuron dynamics in hippocampal networks.Elife. 2015; 4e04378Crossref Scopus (48) Google Scholar]. Large-scale, long-term recordings of firing in different neural circuits with distinct functions in behaving animals will help to determine the scale of firing rate regulation in distinct neural circuits. In the case of circuit-wide homeostatic regulation, does it involve all cell types? In vivo single-unit recordings in rat V1 show MFR renormalization at the level of regular spiking units (mixed pyramidal neurons and interneurons) and of fast spiking units (mainly attributed to parvalbumin-positive interneurons) during the critical period [15.Hengen K.B. et al.Firing rate homeostasis in visual cortex of freely behaving rodents.Neuron. 2013; 80: 335-342Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar]. By contrast, the same brain area in adult mice shows renormalization of somatic Ca2+ rates (reflecting spike rates) in excitatory, but not inhibitory, neurons [17.Barnes S.J. et al.Subnetwork-specific homeostatic plasticity in mouse visual cortex in vivo.Neuron. 2015; 86: 1290-1303Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]. Thus, the type of neurons underlying network-level homeostatic MFR recovery may depend on the specific brain region and developmental stage. A more suitable potential candidate for global stabilization of firing properties at the population level is astrocytes. Astrocytes have emerged as active players in brain energy delivery, production, utilization, and storage [26.Bélanger M. et al.Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation.Cell Metab. 2011; 14: 724-738Abstract Full Text Full Text PDF PubMed Scopus (1048) Google Scholar]. Each astrocyte covers a defined territory with little overlap between neighboring astrocytes, each contacting ~140 000 synapses in the rodent hippocampus [27.Araque A. et al.Gliotransmitters travel in time and space.Neuron. 2014; 81: 728-739Abstract Full Text Full Text PDF PubMed Scopus (585) Google Scholar]. In response to synaptic activity, astrocytes may release gliotransmitters, cytokines, and metabolites that can feed back globally on a large number of synapses and neurons. For example, release of cytokine tumor necrosis factor (TNF) α by glia underlies homeostatic postsynaptic upscaling in response to chronic activity reduction [28.Stellwagen D. Malenka R.C. Synaptic scaling mediated by glial TNF-α.Nature. 2006; 440: 1054-1059Crossref PubMed Scopus (1123) Google Scholar]. Moreover, astrocytes are interconnected through gap junctions, forming a large, dynamic network that can sense summed electrical activity across many neurons and elicit a broad feedback response. There is likely no singular homeostatic effector used to stabilize MFR. Computational work by Eve Marder and colleagues predicted that widely disparate sets of synaptic and intrinsic excitability mechanisms underlie virtually indistinguishable patterns of network activity [8.Marder E. Goaillard J.M. Variability, compensation and homeostasis in neuron and network function.Nat. Rev. Neurosci. 2006; 7: 563-574Crossref PubMed Scopus (696) Google Scholar,29.Prinz A.A. et al.Similar network activity from disparate circuit parameters.Nat. Neurosci. 2004; 7: 1345-1352Crossref PubMed Scopus (555) Google Scholar]. Synaptic scaling, regulated by the abundance of AMPA receptors at dendritic spines, is one of the most extensively studied homeostatic effectors [9.Turrigiano G.G. et al.Activity-dependent scaling of quantal amplitude in neocortical neurons.Nature. 1998; 391: 892-896Crossref PubMed Scopus (1505) Google Scholar,30.O'Brien R.J. et al.Activity-dependent modulation of synaptic AMPA receptor accumulation.Neuron. 1998; 21: 1067-1078Abstract Full Text Full Text PDF PubMed Scopus (515) Google Scholar], but today a wide repertoire of homeostatic mechanisms has been identified, including changes in synaptic release probability, intrinsic excitability, and excitation-to-inhibition ratio [18.Keck T. et al.Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions.Philos. Trans. R. Soc. Lond. B Biol. Sci. 2017; 37220160158Crossref PubMed Scopus (79) Google Scholar,25.Maffei A. Fontanini A. Network homeostasis: a matter of coordination.Curr. Opin. Neurobiol. 2009; 19: 168-173Crossref PubMed Scopus (71) Google Scholar,31.Davis G.W. Homeostatic signaling and the stabilization of neural function.Neuron. 2013; 80: 718-728Abstract Full Text Full Text PDF PubMed Scopus (159) Google Scholar, 32.Turrigiano G. Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement.Annu. Rev. Neurosci. 2011; 34: 89-103Crossref PubMed Scopus (411) Google Scholar, 33.Pozo K. Goda Y. Unraveling mechanisms of homeostatic synaptic plasticity.Neuron. 2010; 66: 337-351Abstract Full Text Full Text PDF PubMed Scopus (354) Google Scholar]. Furthermore, observation of a specific form of homeostatic plasticity (such as synaptic scaling) alone is insufficient to ensure MFR renormalization following a perturbation. Continuous measurement of a regulated variable such as MFR is essential to determine the sufficiency of specific neural network homeostatic mechanisms. Perturbations to brain activity can be categorized into two classes based on their effects on neural activity. The majority are Type 1; they change MFR only transiently because they do not impair the essential components of homeostatic regulation that cause MFR stabilization (Figure 1B,C). Examples include a constant increase in GABA spillover via inhibition of GABA transporters [11.Slomowitz E. et al.Interplay between population firing stability and single neuron dynamics in hippocampal networks.Elife. 2015; 4e04378Crossref Scopus (48) Google Scholar] and a constant increase in glutamate spillover via inhibition of glutamate transporters [13.Styr B. et al.Mitochondrial regulation of the hippocampal firing rate set point and seizure susceptibility.Neuron. 2019; 102: 1009-1024.e8Abstract Full Text Full Text PDF PubMed Scopus (26) Google Scholar]. These perturbations inhibit and potentiate MFR, respectively, but result in MFR recovery typically after 2 days. By contrast, neural circuits cannot compensate for changes to MFR in the presence of Type 2 perturbations, because they disturb necessary components of the homeostatic machinery. For example, blockade of GABAB receptors impairs MFR compensation to hyperactivity [12.Vertkin I. et al.GABAB receptor deficiency causes failure of neuronal homeostasis in hippocampal networks.Proc. Natl. Acad. Sci. U. S. A. 2015; 112: E3291-E3299Crossref PubMed Scopus (24) Google Scholar], suggesting that GABAB receptors are necessary for homeostatic regulation of MFR. Discovering Type 2 perturbations that enable persistent changes to MFR will help identify the molecular members of the core MFR homeostasis machinery. Because Type 2 perturbations may target different nodes of the homeostatic machinery, they can only be discovered using a 'dual-challenge' approach where they are implemented in conjunction with known Type 1 perturbations that result in adequate compensation of MFR when presented on their own. A Type 2 perturbation that results in a persistent change in MFR without perturbing the homeostatic response to a Type 1 perturbation can be classified as a direct regulator of MFR set point (Figure 1D). In this case, the compensatory mechanisms triggered by the Type 1 perturbation act in reference to a new set-point value. However, if a Type 2 perturbation specifically impairs renormalization of MFR to a set point without affecting MFR on its own, it can be classified as an MFR homeostatic response regulator (Figure 1E), indirectly resulting in a set-point change. Shank3, a postsynaptic density scaffolding protein, is an example of an MFR homeostatic response regulator because its knockdown impairs MFR compensation to inactivity [34.Tatavarty V. et al.Autism-associated Shank3 is essential for homeostatic compensation in rodent V1.Neuron. 2020; 106: 769-777.e4Abstract Full Text Full Text PDF PubMed Scopus (18) Google Scholar]. Thus, the dual-challenge approach enables identification of distinct sources of homeostatic failures. Vigilance state and firing rate properties covary, which suggests that firing rate homeostasis is state dependent in nature [35.Watson B.O. et al.Network homeostasis and state dynamics of neocortical sleep.Neuron. 2016; 90: 839-852Abstract Full Text Full Text PDF PubMed Google Scholar,36.Levenstein D. et al.Sleep regulation of the distribution of cortical firing rates.Curr. Opin. Neurobiol. 2017; 44: 34-42Crossref PubMed Scopus (24) Google Scholar]. The firing rate of single neurons differs across behavioral states such as active and quiet wake and rapid eye movement (REM) and nonrapid eye movement (NREM) sleep. However, these state transitions affect firing rate differently across brain regions. In sleep-promoting brain areas such as the supraoptic nucleus in the hypothalamus, MFRs are increased during sleep [37.Jiang-Xie L.-F. et al.A common neuroendocrine substrate for diverse general anesthetics and sleep.Neuron. 2019; 102: 1053-1065.e4Abstract Full Text Full Text PDF PubMed Scopus (36) Google Scholar]. In some but not all circuits of the neocortex and hippocampus, MFRs decrease during sleep but return to apparent state-dependent set points following transitions into higher vigilance states [38.Vyazovskiy V.V. et al.Cortical firing and sleep homeostasis.Neuron. 2009; 63: 865-878Abstract Full Text Full Text PDF PubMed Scopus (433) Google Scholar, 39.Miyawaki H. Diba K. Regulation of hippocampal firing by network oscillations during sleep.Curr. Biol. 2016; 26: 893-902Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar, 40.Zhou H. et al.Cholinergic modulation of hippocampal calcium activity across the sleep-wake cycle.Elife. 2019; 8e39777Crossref PubMed Scopus (4) Google Scholar]. Notably, MFR is reduced during NREM sleep in deep, but not superficial, cortical layers of V1 [41.Senzai Y. et al.Layer-specific physiological features and interlaminar interactions in the primary visual cortex of the mouse.Neuron. 2019; 101: 500-513.e5Abstract Full Text Full Text PDF PubMed Scopus (39) Google Scholar]. These results suggest that state-dependent mechanisms of MFR homeostasis are inducted locally within neural circuits. Other parameters of firing rate statistics are also hypothesized to be homeostatically regulated by sleep. For example, the distribution of firing rates across neurons in several cortical areas becomes narrower during NREM sleep, displaying a preferential reduction in MFR of highly active neurons alongside an increase in MFR of low-firing neurons [35.Watson B.O. et al.Network homeostasis and state dynamics of neocortical sleep.Neuron. 2016; 90: 839-852Abstract Full Text Full Text PDF PubMed Google Scholar]. Interestingly, this reflects the change in firing rate distributions in the barrel cortex following whisker deprivation, when homeostatic processes are engaged to stabilize firing after sensory inputs are reduced [42.Margolis D.J. et al.Reorganization of cortical population activity imaged throughout long-term sensory deprivation.Nat. Neurosci. 2012; 15: 1539-1546Crossref PubMed Scopus (123) Google Scholar]. These changes to broad network statistics are proposed to coincidentally support plasticity-dependent mnemonic processes and network stability [36.Levenstein D. et al.Sleep regulation of the distribution of cortical firing rates.Curr. Opin. Neurobiol. 2017; 44: 34-42Crossref PubMed Scopus (24) Google Scholar], thus solving the 'plasticity–stability' problem. Recent studies suggest that induction of distinct homeostatic mechanisms, such as those that compensate for a constant decrease versus increase in firing rate, occurs in separate vigilance states. Homeostatic MFR recovery in the rodent V1 in response to reduced sensory input (monocular deprivation) occurs during active wakefulness [20.Hengen K.B. et al.Neuronal firing rate homeostasis is inhibited by sleep and promoted by wake.Cell. 2016; 165: 180-191Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar], while the homeostatic response to hyperactivity (opening of a closed eye after monocular deprivation) occurs during sleep [43.Torrado Pacheco A. et al.Sleep promotes downward firing rate homeostasis.Neuron. 2021; 109: 530-544.e6Abstract Full Text Full Text PDF PubMed Scopus (8) Google Scholar]. As monocular MFR across all the cortical layers is state independent in V1 [15.Hengen K.B. et al.Firing rate homeostasis in visual cortex of freely behaving rodents.Neuron. 2013; 80: 335-342Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar,20.Hengen K.B. et al.Neuronal firing rate homeostasis is inhibited by sleep and promoted by wake.Cell. 2016; 165: 180-191Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar], whether similar rules operate in circuits with state-dependent MFR dynamics remains an open question. Specifically, it would be important to demonstrate whether state-dependent MFR set points at the population level are homeostatically maintained following chronic perturbations and whether similar induction rules operate there. Different homeostatic effectors operate to enable MFR homeostasis across sleep–wake states. Downscaling of excitatory synapses through AMPA receptor removal and shrinkage of spine head volume was identified during normal sleep or recovery sleep following sleep deprivation [44.Tononi G. Cirelli C. Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.Neuron. 2014; 81: 12-34Abstract Full Text Full Text PDF PubMed Scopus (889) Google Scholar, 45.de Vivo L. et al.Ultrastructural evidence for synaptic scaling across the wake/sleep cycle.Science. 2017; 355: 507-510Crossref PubMed Scopus (227) Google Scholar, 46.Diering G.H. et al.Homer1a drives homeostatic scaling-down of excitatory synapses during sleep.Science. 2017; 355: 511-515Crossref PubMed Scopus (198) Google Scholar, 47.Cirelli C. Sleep, synaptic homeostasis and neuronal firing rates.Curr. Opin. Neurobiol. 2017; 44: 72-79Crossref PubMed Scopus (22) Google Scholar]. These results support a hypothesis that sleep induces homeostatic synaptic downscaling to restore the net synaptic strength challenged by synaptic potentiation during wakefulness [44.Tononi G. Cirelli C. Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.Neuron. 2014; 81: 12-34Abstract Full Text Full Text PDF PubMed Scopus (889) Google Scholar]. However, it is still unknown if total excitatory synaptic strength across a dendritic tree is a regulated variable that is homeostatically maintained or if synaptic scaling of excitatory synapses alone is sufficient for homeostatic recovery of firing rate distributions following a perturbation. In addition to synaptic scaling, sleep deprivation has been shown to increase intrinsic neuronal excitability [48.Liu S. et al.Sleep drive is encoded by neural plastic changes in a dedicated circuit.Cell. 2016; 165: 1347-1360Abstract Full Text Full Text PDF PubMed Scopus (136) Google Scholar] and augment miniature excitatory postsynaptic current (mEPSC) frequency [49.Liu Z.-W. et al.Direct evidence for wake-related increases and sleep-related decreases in synaptic strength in rodent cortex.J. Neurosci. 2010; 30: 8671-8675Crossref PubMed Scopus (141) Google Scholar]. This suggests that a decrease in intrinsic neuronal excitability, excitatory quantal synaptic transmission, and excitation-to-inhibition ratio may all contribute to maintenance of lower network MFR set points during sleep. Sleep pressure represents another physiological variable that is under homeostatic regulation. Since distinct homeostatic mechanisms are induced in sleep and wake states, sleep pressure may serve a role in stabilizing MFR. Sleep homeostasis is reflected by a compensatory increase in sleep duration and in the intensity of sleep, measured as the levels of slow-wave activity during NREM sleep, after extended wakefulness (Box 1). MFR correlates with sleep need in rats [38.Vyazovskiy V.V. et al.Cortical firing and sleep homeostasis.Neuron. 2009; 63: 865-878Abstract Full Text Full Text PDF PubMed Scopus (433) Google Scholar] and flies [39.Miyawaki H. Diba K. Regulation of hippocampal firing by network oscillations during sleep.Curr. Biol. 2016; 26: 893-902Abstract Full Text Full Text PDF PubMed Scopus (57) Google Scholar], suggesting that this r