Adityanand Guntuboyina, Donovan Lieu, Sabyasachi Chatterjee, Bodhisattva Sen.

Source: The Annals of Statistics, Volume 48, Number 1, 205--229.

Abstract:
We study trend filtering, a relatively recent method for univariate nonparametric regression. For a given integer $rgeq1$, the $r$th order trend filtering estimator is defined as the minimizer of the sum of squared errors when we constrain (or penalize) the sum of the absolute $r$th order discrete derivatives of the fitted function at the design points. For $r=1$, the estimator reduces to total variation regularization which has received much attention in the statistics and image processing literature. In this paper, we study the performance of the trend filtering estimator for every $rgeq1$, both in the constrained and penalized forms. Our main results show that in the strong sparsity setting when the underlying function is a (discrete) spline with few “knots,” the risk (under the global squared error loss) of the trend filtering estimator (with an appropriate choice of the tuning parameter) achieves the parametric $n^{-1}$-rate, up to a logarithmic (multiplicative) factor. Our results therefore provide support for the use of trend filtering, for every $rgeq1$, in the strong sparsity setting.

Kai Tan, Lei Shi, Zhou Yu.

Source: The Annals of Statistics, Volume 48, Number 1, 64--85.

Abstract:
Sliced inverse regression (SIR) is an innovative and effective method for sufficient dimension reduction and data visualization. Recently, an impressive range of penalized SIR methods has been proposed to estimate the central subspace in a sparse fashion. Nonetheless, few of them considered the sparse sufficient dimension reduction from a decision-theoretic point of view. To address this issue, we in this paper establish the minimax rates of convergence for estimating the sparse SIR directions under various commonly used loss functions in the literature of sufficient dimension reduction. We also discover the possible trade-off between statistical guarantee and computational performance for sparse SIR. We finally propose an adaptive estimation scheme for sparse SIR which is computationally tractable and rate optimal. Numerical studies are carried out to confirm the theoretical properties of our proposed methods.

Xin Bing, Marten H. Wegkamp.

Source: The Annals of Statistics, Volume 47, Number 6, 3157--3184.

Abstract:
We consider the multivariate response regression problem with a regression coefficient matrix of low, unknown rank. In this setting, we analyze a new criterion for selecting the optimal reduced rank. This criterion differs notably from the one proposed in Bunea, She and Wegkamp ( Ann. Statist. 39 (2011) 1282–1309) in that it does not require estimation of the unknown variance of the noise, nor does it depend on a delicate choice of a tuning parameter. We develop an iterative, fully data-driven procedure, that adapts to the optimal signal-to-noise ratio. This procedure finds the true rank in a few steps with overwhelming probability. At each step, our estimate increases, while at the same time it does not exceed the true rank. Our finite sample results hold for any sample size and any dimension, even when the number of responses and of covariates grow much faster than the number of observations. We perform an extensive simulation study that confirms our theoretical findings. The new method performs better and is more stable than the procedure of Bunea, She and Wegkamp ( Ann. Statist. 39 (2011) 1282–1309) in both low- and high-dimensional settings.

Kerstin Spitzer, Marta Pelizzola, Andreas Futschik.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 202--220.

Abstract:
Evolve and resequence studies provide a popular approach to simulate evolution in the lab and explore its genetic basis. In this context, Pearson’s chi-square test, Fisher’s exact test as well as the Cochran–Mantel–Haenszel test are commonly used to infer genomic positions affected by selection from temporal changes in allele frequency. However, the null model associated with these tests does not match the null hypothesis of actual interest. Indeed, due to genetic drift and possibly other additional noise components such as pool sequencing, the null variance in the data can be substantially larger than accounted for by these common test statistics. This leads to $p$-values that are systematically too small and, therefore, a huge number of false positive results. Even, if the ranking rather than the actual $p$-values is of interest, a naive application of the mentioned tests will give misleading results, as the amount of overdispersion varies from locus to locus. We therefore propose adjusted statistics that take the overdispersion into account while keeping the formulas simple. This is particularly useful in genome-wide applications, where millions of SNPs can be handled with little computational effort. We then apply the adapted test statistics to real data from Drosophila and investigate how information from intermediate generations can be included when available. We also discuss further applications such as genome-wide association studies based on pool sequencing data and tests for local adaptation.

Hannah Worthington, Rachel McCrea, Ruth King, Richard Griffiths.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2043--2064.

Abstract:
Capture-recapture studies often involve collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example, annually, resulting in capture information spanning multiple sampling periods. To account for the different temporal scales, the robust design class of models have traditionally been applied providing a framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically require strong constraints, either the assumption of closure within a sampling period (the closed robust design) or conditioning on the number of individuals captured within a sampling period (the open robust design). For real datasets these assumptions may not be appropriate. We develop a general modelling structure that requires neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance within a single consistent framework. The flexibility of the novel model structure is further demonstrated by including the computationally challenging case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the significant improvement in parameter estimation using our new modelling approach in terms of both the multi-period and multi-state components through both a simulation study and a real dataset relating to the protected species of great crested newts, Triturus cristatus .

Ye Zhang, Zhigang Yao, Patrik Forssén, Torgny Fornstedt.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2011--2042.

Abstract:
The means to obtain the rate constants of a chemical reaction is a fundamental open problem in both science and the industry. Traditional techniques for finding rate constants require either chemical modifications of the reactants or indirect measurements. The rate constant map method is a modern technique to study binding equilibrium and kinetics in chemical reactions. Finding a rate constant map from biosensor data is an ill-posed inverse problem that is usually solved by regularization. In this work, rather than finding a deterministic regularized rate constant map that does not provide uncertainty quantification of the solution, we develop an adaptive variational Bayesian approach to estimate the distribution of the rate constant map, from which some intrinsic properties of a chemical reaction can be explored, including information about rate constants. Our new approach is more realistic than the existing approaches used for biosensors and allows us to estimate the dynamics of the interactions, which are usually hidden in a deterministic approximate solution. We verify the performance of the new proposed method by numerical simulations, and compare it with the Markov chain Monte Carlo algorithm. The results illustrate that the variational method can reliably capture the posterior distribution in a computationally efficient way. Finally, the developed method is also tested on the real biosensor data (parathyroid hormone), where we provide two novel analysis tools—the thresholding contour map and the high order moment map—to estimate the number of interactions as well as their rate constants.

Cristina Butucea, Amandine Dubois, Martin Kroll, Adrien Saumard.

Source: Bernoulli, Volume 26, Number 3, 1727--1764.

Abstract:
We address the problem of non-parametric density estimation under the additional constraint that only privatised data are allowed to be published and available for inference. For this purpose, we adopt a recent generalisation of classical minimax theory to the framework of local $alpha$-differential privacy and provide a lower bound on the rate of convergence over Besov spaces $mathcal{B}^{s}_{pq}$ under mean integrated $mathbb{L}^{r}$-risk. This lower bound is deteriorated compared to the standard setup without privacy, and reveals a twofold elbow effect. In order to fulfill the privacy requirement, we suggest adding suitably scaled Laplace noise to empirical wavelet coefficients. Upper bounds within (at most) a logarithmic factor are derived under the assumption that $alpha$ stays bounded as $n$ increases: A linear but non-adaptive wavelet estimator is shown to attain the lower bound whenever $pgeq r$ but provides a slower rate of convergence otherwise. An adaptive non-linear wavelet estimator with appropriately chosen smoothing parameters and thresholding is shown to attain the lower bound within a logarithmic factor for all cases.

Fangzheng Xie, Yanxun Xu.

Source: Bayesian Analysis, Volume 15, Number 1, 159--186.

Abstract:
We propose a kernel mixture of polynomials prior for Bayesian nonparametric regression. The regression function is modeled by local averages of polynomials with kernel mixture weights. We obtain the minimax-optimal contraction rate of the full posterior distribution up to a logarithmic factor by estimating metric entropies of certain function classes. Under the assumption that the degree of the polynomials is larger than the unknown smoothness level of the true function, the posterior contraction behavior can adapt to this smoothness level provided an upper bound is known. We also provide a frequentist sieve maximum likelihood estimator with a near-optimal convergence rate. We further investigate the application of the kernel mixture of polynomials to partial linear models and obtain both the near-optimal rate of contraction for the nonparametric component and the Bernstein-von Mises limit (i.e., asymptotic normality) of the parametric component. The proposed method is illustrated with numerical examples and shows superior performance in terms of computational efficiency, accuracy, and uncertainty quantification compared to the local polynomial regression, DiceKriging, and the robust Gaussian stochastic process.

Brian Zingg
Apr 15, 2020; 40:3250-3267
Systems/Circuits

Sebastian Onasch
Apr 15, 2020; 40:3186-3202
Systems/Circuits

Xiao-Jing Wang
Oct 15, 1996; 16:6402-6413
Articles

KM Harris
Jul 1, 1992; 12:2685-2705
Articles

R Shadmehr
May 1, 1994; 14:3208-3224
Articles

Lennart Mucke
Jun 1, 2000; 20:4050-4058
Cellular

Guo-qiang Bi
Dec 15, 1998; 18:10464-10472
Articles

Customer service doesn't have to suffer while your company goes through a difficult transition. In fact, 78 percent of consumers said they stopped doing business with a company because of poor customer service. Now is the time to show your most valuable buyers how you'll keep them informed, updated and respected throughout this crisis by adapting your practices to the moment.

Traditional views of sensorimotor adaptation (i.e., adaptation of movements to perturbed sensory feedback) emphasize the role of automatic, implicit correction of sensory prediction errors. However, latent memories formed during sensorimotor adaptation, manifest as improved relearning (e.g., savings), have recently been attributed to strategic corrections of task errors (failures to achieve task goals). To dissociate contributions of task errors and sensory prediction errors to latent sensorimotor memories, we perturbed target locations to remove or enforce task errors during learning and/or test, with male/female human participants. Adaptation improved after learning in all conditions where participants were permitted to correct task errors, and did not improve whenever we prevented correction of task errors. Thus, previous correction of task errors was both necessary and sufficient to improve adaptation. In contrast, a history of sensory prediction errors was neither sufficient nor obligatory for improved adaptation. Limiting movement preparation time showed that the latent memories driven by learning to correct task errors take at least two forms: a time-consuming but flexible component, and a rapidly expressible, inflexible component. The results provide strong support for the idea that movement corrections driven by a failure to successfully achieve movement goals underpin motor memories that manifest as savings. Such persistent memories are not exclusively mediated by time-consuming strategic processes but also comprise a rapidly expressible but inflexible component. The distinct characteristics of these putative processes suggest dissociable underlying mechanisms, and imply that identification of the neural basis for adaptation and savings will require methods that allow such dissociations.

SIGNIFICANCE STATEMENT Latent motor memories formed during sensorimotor adaptation manifest as improved adaptation when sensorimotor perturbations are reencountered. Conflicting theories suggest that this "savings" is underpinned by different mechanisms, including a memory of successful actions, a memory of errors, or an aiming strategy to correct task errors. Here we show that learning to correct task errors is sufficient to show improved subsequent adaptation with respect to naive performance, even when tested in the absence of task errors. In contrast, a history of sensory prediction errors is neither sufficient nor obligatory for improved adaptation. Finally, we show that latent sensorimotor memories driven by task errors comprise at least two distinct components: a time-consuming, flexible component, and a rapidly expressible, inflexible component.

The cerebellum influences motor control through Purkinje target neurons, which transmit cerebellar output. Such output is required, for instance, for larval zebrafish to learn conditioned fictive swimming. The output cells, called eurydendroid neurons (ENs) in teleost fish, are inhibited by Purkinje cells and excited by parallel fibers. Here, we investigated the electrophysiological properties of glutamatergic ENs labeled by the transcription factor olig2. Action potential firing and synaptic responses were recorded in current clamp and voltage clamp from olig2⁺ neurons in immobilized larval zebrafish (before sexual differentiation) and were correlated with motor behavior by simultaneous recording of fictive swimming. In the absence of swimming, olig2⁺ ENs had basal firing rates near 8 spikes/s, and EPSCs and IPSCs were evident. Comparing Purkinje firing rates and eurydendroid IPSC rates indicated that 1-3 Purkinje cells converge onto each EN. Optogenetically suppressing Purkinje simple spikes, while preserving complex spikes, suggested that eurydendroid IPSC size depended on presynaptic spike duration rather than amplitude. During swimming, EPSC and IPSC rates increased. Total excitatory and inhibitory currents during sensory-evoked swimming were both more than double those during spontaneous swimming. During both spontaneous and sensory-evoked swimming, the total inhibitory current was more than threefold larger than the excitatory current. Firing rates of ENs nevertheless increased, suggesting that the relative timing of IPSCs and EPSCs may permit excitation to drive additional eurydendroid spikes. The data indicate that olig2⁺ cells are ENs whose activity is modulated with locomotion, suiting them to participate in sensorimotor integration associated with cerebellum-dependent learning.

SIGNIFICANCE STATEMENT The cerebellum contributes to movements through signals generated by cerebellar output neurons, called eurydendroid neurons (ENs) in fish (cerebellar nuclei in mammals). ENs receive sensory and motor signals from excitatory parallel fibers and inhibitory Purkinje cells. Here, we report electrophysiological recordings from ENs of larval zebrafish that directly illustrate how synaptic inhibition and excitation are integrated by cerebellar output neurons in association with motor behavior. The results demonstrate that inhibitory and excitatory drive both increase during fictive swimming, but inhibition greatly exceeds excitation. Firing rates nevertheless increase, providing evidence that synaptic integration promotes cerebellar output during locomotion. The data offer a basis for comparing aspects of cerebellar coding that are conserved and that diverge across vertebrates.

Revealing the organization and function of neural circuits is greatly facilitated by viral tools that spread transsynaptically. Adeno-associated virus (AAV) exhibits anterograde transneuronal transport, however, the synaptic specificity of this spread and its broad application within a diverse set of circuits remains to be explored. Here, using anatomic, functional, and molecular approaches, we provide evidence for the preferential transport of AAV1 to postsynaptically connected neurons and reveal its spread is strongly dependent on synaptic transmitter release. In addition to glutamatergic pathways, AAV1 also spreads through GABAergic synapses to both excitatory and inhibitory cell types. We observed little or no transport, however, through neuromodulatory projections (e.g., serotonergic, cholinergic, and noradrenergic). In addition, we found that AAV1 can be transported through long-distance descending projections from various brain regions to effectively transduce spinal cord neurons. Combined with newly designed intersectional and sparse labeling strategies, AAV1 can be applied within a wide variety of pathways to categorize neurons according to their input sources, morphology, and molecular identities. These properties make AAV1 a promising anterograde transsynaptic tool for establishing a comprehensive cell-atlas of the brain, although its capacity for retrograde transport currently limits its use to unidirectional circuits.

SIGNIFICANCE STATEMENT The discovery of anterograde transneuronal spread of AAV1 generates great promise for its application as a unique tool for manipulating input-defined cell populations and mapping their outputs. However, several outstanding questions remain for anterograde transsynaptic approaches in the field: (1) whether AAV1 spreads exclusively or specifically to synaptically connected neurons, and (2) how broad its application could be in various types of neural circuits in the brain. This study provides several lines of evidence in terms of anatomy, functional innervation, and underlying mechanisms, to strongly support that AAV1 anterograde transneuronal spread is highly synapse specific. In addition, several potentially important applications of transsynaptic AAV1 in probing neural circuits are described.

Giving birth triggers a wide repertoire of physiological and behavioral changes in the mother to enable her to feed and care for her offspring. These changes require coordination and are often orchestrated from the CNS, through as of yet poorly understood mechanisms. A neuronal population with a central role in puerperal changes is the tuberoinfundibular dopamine (TIDA) neurons that control release of the pituitary hormone, prolactin, which triggers key maternal adaptations, including lactation and maternal care. Here, we used Ca²⁺ imaging on mice from both sexes and whole-cell recordings on female mouse TIDA neurons in vitro to examine whether they adapt their cellular and network activity according to reproductive state. In the high-prolactin state of lactation, TIDA neurons shift to faster membrane potential oscillations, a reconfiguration that reverses upon weaning. During the estrous cycle, however, which includes a brief, but pronounced, prolactin peak, oscillation frequency remains stable. An increase in the hyperpolarization-activated mixed cation current, I_h, possibly through unmasking as dopamine release drops during nursing, may partially explain the reconfiguration of TIDA rhythms. These findings identify a reversible plasticity in hypothalamic network activity that can serve to adapt the dam for motherhood.

SIGNIFICANCE STATEMENT Motherhood requires profound behavioral and physiological adaptations to enable caring for offspring, but the underlying CNS changes are poorly understood. Here, we show that, during lactation, neuroendocrine dopamine neurons, the "TIDA" cells that control prolactin secretion, reorganize their trademark oscillations to discharge in faster frequencies. Unlike previous studies, which typically have focused on structural and transcriptional changes during pregnancy and lactation, we demonstrate a functional switch in activity and one that, distinct from previously described puerperal modifications, reverses fully on weaning. We further provide evidence that a specific conductance (I_h) contributes to the altered network rhythm. These findings identify a new facet of maternal brain plasticity at the level of membrane properties and consequent ensemble activity.

Neurons and circuits each with a distinct balance of intrinsic and synaptic conductances can generate similar behavior but sometimes respond very differently to perturbation. Examining a large family of circuit models with non-identical neurons and synapses underlying rhythmic behavior, we analyzed the circuits' response to modifications in single and multiple intrinsic conductances in the individual neurons. To summarize these changes over the entire range of perturbed parameters, we quantified circuit output by defining a global stability measure. Using this measure, we identified specific subsets of conductances that when perturbed generate similar behavior in diverse individuals of the population. Our unbiased clustering analysis enabled us to quantify circuit stability when simultaneously perturbing multiple conductances as a nonlinear combination of single conductance perturbations. This revealed surprising conductance combinations that can predict the response to specific perturbations, even when the remaining intrinsic and synaptic conductances are unknown. Therefore, our approach can expose hidden variability in the balance of intrinsic and synaptic conductances of the same neurons across different versions of the same circuit solely from the circuit response to perturbations. Developed for a specific family of model circuits, our quantitative approach to characterizing high-dimensional degenerate systems provides a conceptual and analytic framework to guide future theoretical and experimental studies on degeneracy and robustness.

SIGNIFICANCE STATEMENT Neural circuits can generate nearly identical behavior despite neuronal and synaptic parameters varying several-fold between individual instantiations. Yet, when these parameters are perturbed through channel deletions and mutations or environmental disturbances, seemingly identical circuits can respond very differently. What distinguishes inconsequential perturbations that barely alter circuit behavior from disruptive perturbations that drastically disturb circuit output remains unclear. Focusing on a family of rhythmic circuits, we propose a computational approach to reveal hidden variability in the intrinsic and synaptic conductances in seemingly identical circuits based solely on circuit output to different perturbations. We uncover specific conductance combinations that work similarly to maintain stability and predict the effect of changing multiple conductances simultaneously, which often results from neuromodulation or injury.

Dopaminergic neurons innervate extensive areas of the brain and release dopamine (DA) onto a wide range of target neurons. However, DA release is also precisely regulated. In Drosophila melanogaster brain explant preparations, DA is released specifically onto α3/α'3 compartments of mushroom body (MB) neurons that have been coincidentally activated by cholinergic and glutamatergic inputs. The mechanism for this precise release has been unclear. Here we found that coincidentally activated MB neurons generate carbon monoxide (CO), which functions as a retrograde signal evoking local DA release from presynaptic terminals. CO production depends on activity of heme oxygenase in postsynaptic MB neurons, and CO-evoked DA release requires Ca²⁺ efflux through ryanodine receptors in DA terminals. CO is only produced in MB areas receiving coincident activation, and removal of CO using scavengers blocks DA release. We propose that DA neurons use two distinct modes of transmission to produce global and local DA signaling.

SIGNIFICANCE STATEMENT Dopamine (DA) is needed for various higher brain functions, including memory formation. However, DA neurons form extensive synaptic connections, while memory formation requires highly specific and localized DA release. Here we identify a mechanism through which DA release from presynaptic terminals is controlled by postsynaptic activity. Postsynaptic neurons activated by cholinergic and glutamatergic inputs generate carbon monoxide, which acts as a retrograde messenger inducing presynaptic DA release. Released DA is required for memory-associated plasticity. Our work identifies a novel mechanism that restricts DA release to the specific postsynaptic sites that require DA during memory formation.

Calcineurin inhibitors, such as tacrolimus (FK506) and cyclosporine, are widely used as standard immunosuppressants in organ transplantation recipients. However, these drugs can cause severe pain in patients, commonly referred to as calcineurin inhibitor-induced pain syndrome (CIPS). Although calcineurin inhibition increases NMDAR activity in the spinal cord, the underlying mechanism remains enigmatic. Using an animal model of CIPS, we found that systemic administration of FK506 in male and female mice significantly increased the amount of α2-1–GluN1 complexes in the spinal cord and the level of α2-1–bound GluN1 proteins in spinal synaptosomes. Treatment with FK506 significantly increased the frequency of mEPSCs and the amplitudes of monosynaptic EPSCs evoked from the dorsal root and puff NMDAR currents in spinal dorsal horn neurons. Inhibiting α2-1 with gabapentin or disrupting the α2-1–NMDAR interaction with α2-1Tat peptide completely reversed the effects of FK506. In α2-1 gene KO mice, treatment with FK506 failed to increase the frequency of NMDAR-mediated mEPSCs and the amplitudes of evoked EPSCs and puff NMDAR currents in spinal dorsal horn neurons. Furthermore, systemic administration of gabapentin or intrathecal injection of α2-1Tat peptide reversed thermal and mechanical hypersensitivity in FK506-treated mice. In addition, genetically deleting GluN1 in dorsal root ganglion neurons or α2-1 genetic KO similarly attenuated FK506-induced thermal and mechanical hypersensitivity. Together, our findings indicate that α2-1–bound NMDARs mediate calcineurin inhibitor-induced tonic activation of presynaptic and postsynaptic NMDARs at the spinal cord level and that presynaptic NMDARs play a prominent role in the development of CIPS.

SIGNIFICANCE STATEMENT Calcineurin inhibitors are immunosuppressants used to prevent rejection of transplanted organs and tissues. However, these drugs can cause severe, unexplained pain. We showed that calcineurin inhibition enhances physical interaction between α2-1 and NMDARs and their synaptic trafficking in the spinal cord. α2-1 is essential for calcineurin inhibitor-induced aberrant activation of presynaptic and postsynaptic NMDARs in the spinal cord. Furthermore, inhibiting α2-1 or disrupting α2-1–NMDAR interaction reduces calcineurin inhibitor-induced pain hypersensitivity. Eliminating NMDARs in primary sensory neurons or α2-1 KO also attenuates calcineurin inhibitor-induced pain hypersensitivity. This new information extends our mechanistic understanding of the role of endogenous calcineurin in regulating synaptic plasticity and nociceptive transmission and suggests new strategies for treating this painful condition.

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_v1.6 and K_v7.2 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.

SIGNIFICANCE STATEMENT Persistent alterations in synaptic input elicit homeostatic plastic changes in neuronal activity. Here we show that chronic neuronal hyperexcitability, induced by M-type potassium channel inhibition, triggered intrinsic and synaptic homeostatic plasticity at different timescales in hippocampal excitatory neurons. The data indicate that the fast adaptation of intrinsic excitability depends on ongoing spiking activity but is not sufficient to provide homeostasis of the mean firing rate. Our results show that a single perturbation such as M-channel inhibition can trigger multiple homeostatic processes that operate at different timescales to maintain network mean firing rate.

Have you ever thought about producing a video on food, nutrition, sustainability or hunger? Whether you’re a food buff, a student, an activist, movie geek or professional filmmaker, we have just the thing for you.Short Food Movie is a global open call for videos inspired by the theme for Expo Milano 2015, “Feeding the Planet. Energy for Life.”  It includes [...]

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Apple's higher-end 13-inch MacBook Pros are equipped to take advantage of 87W power adapters, though users won't see any charging speed benefits from the change.