Summary Maintaining average activity within a set-point range constitutes a fundamental property of central neural circuits. However, whether and how activity set points are regulated remains unknown. Integrating genome-scale metabolic modeling and experimental study of neuronal homeostasis, we identified mitochondrial dihydroorotate dehydrogenase (DHODH) as a regulator of activity set points in hippocampal networks. The DHODH inhibitor teriflunomide stably suppressed mean firing rates via synaptic and intrinsic excitability mechanisms by modulating mitochondrial Ca 2+ buffering and spare respiratory capacity. Bi-directional activity perturbations under DHODH blockade triggered firing rate compensation, while stabilizing firing to the lower level, indicating a change in the firing rate set point. In vivo , teriflunomide decreased CA3-CA1 synaptic transmission and CA1 mean firing rate and attenuated susceptibility to seizures, even in the intractable Dravet syndrome epilepsy model. Our results uncover mitochondria as a key regulator of activity set points, demonstrate the differential regulation of set points and compensatory mechanisms, and propose a new strategy to treat epilepsy.

Persistent alterations in neuronal activity elicit homeostatic plastic changes in synaptic transmission and/or intrinsic excitability. However, it is unknown whether these homeostatic processes operate in concert or at different temporal scales to maintain network activity around a set-point value. Here we show that chronic neuronal hyperactivity, induced by M-channel inhibition, triggered intrinsic and synaptic homeostatic plasticity at different timescales in cultured hippocampal pyramidal neurons from mice of either sex. Homeostatic changes of intrinsic excitability occurred at a fast timescale (1-4 h) and depended on ongoing spiking activity. This fast intrinsic adaptation included plastic changes in the threshold current and a distal relocation of FGF14, a protein physically bridging Na v 1.6 and K v 7.2 channels along the axon initial segment. In contrast, synaptic adaptations occurred at a slower timescale (;2 d) and involved decreases in miniature EPSC amplitude. To examine how these temporally distinct homeostatic responses influenced hippocampal network activity, we quantified the rate of spontaneous spiking measured by multielectrode arrays at extended timescales. M-Channel blockade triggered slow homeostatic renormalization of the mean firing rate (MFR), concomitantly accompanied by a slow synaptic adaptation. Thus, the fast intrinsic adaptation of excitatory neurons is not sufficient to account for the homeostatic normalization of the MFR. In striking contrast, homeostatic adaptations of intrinsic excitability and spontaneous MFR failed in hippocampal GABAergic inhibitory neurons, which remained hyperexcitable following chronic M-channel blockage. Our results indicate that a single perturbation such as M-channel inhibition triggers multiple homeostatic mechanisms that operate at different timescales to maintain network mean firing rate.

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 neuronal homeostasisThe 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][2][3]. 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 [4,5]. This plasticity-stability problem has been introduced and elegantly reviewed in earlier insightful papers [6][7][8], 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] 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)....

Regulation of firing rate homeostasis constitutes a fundamental property of central neural circuits. While intracellular Ca 2+ has long been hypothesized to be a feedback control signal, the molecular machinery enabling a network-wide homeostatic response remains largely unknown. We show that deletion of insulin-like growth factor-1 receptor (IGF-1R) limits firing rate homeostasis in response to inactivity, without altering the distribution of baseline firing rates. The deficient firing rate homeostatic response was due to disruption of both postsynaptic and intrinsic plasticity. At the cellular level, we detected a fraction of IGF-1Rs in mitochondria, colocalized with the mitochondrial calcium uniporter complex (MCUc). IGF-1R deletion suppressed transcription of the MCUc members and burst-evoked mitochondrial Ca 2+ (mitoCa 2+ ) by weakening mitochondria-to-cytosol Ca 2+ coupling. Overexpression of either mitochondria-targeted IGF-1R or MCUc in IGF-1R–deficient neurons was sufficient to rescue the deficits in burst-to-mitoCa 2+ coupling and firing rate homeostasis. Our findings indicate that mitochondrial IGF-1R is a key regulator of the integrated homeostatic response by tuning the reliability of burst transfer by MCUc. Based on these results, we propose that MCUc acts as a homeostatic Ca 2+ sensor. Faulty activation of MCUc may drive dysregulation of firing rate homeostasis in aging and in brain disorders associated with aberrant IGF-1R/MCUc signaling.

Robust electrical signal propagation in the form of action potentials (AP) is a hallmark of neuronal activity. While it is well established that changes to ionic gradients across the bilayer are responsible for AP propagation, the contributions of ionic diffusion and membrane morphological heterogeneity are not well understood. New experimental imaging methods have already suggested that spatial complexities of dendrites and the spatial dynamics of ionic species influence membrane voltage locally. However, it remains difficult to extract detailed voltage information at short time and length scales, which motivates a need for mathematical models that can incorporate these spatial complexities and predict and dissect voltage dynamics. Owing to the well-mixed assumption employed by popular models such as the Hodgkin-Huxley model, Morris-Lecar model, and cable theory the influence of morphology on voltage propagation cannot be studied. Therefore, building on these models, we construct a spatial model of AP propagation along dendrites which relaxes the well-mixed assumption. We explicitly model local ionic concentrations and their dynamics as influenced by reaction-diffusion and Hodgkin-Huxley type ion channel currents. The local transmembrane potential is determined by the local transmembrane ionic gradient via the Nernst potential. We compare membrane voltage dynamics from this new model to the traditional passive cable equation for dendrites of various sizes and configurations. Using our model, we find that a) membrane voltage propagation depends on the complex geometries of dendrites, b) the presence of internal organelles modulates membrane voltage propagation, and c) downstream signaling dynamics can affect AP propagation.