Salicylate-Induced Hearing Loss Trigger Structural Synaptic Modifications in the Ventral Cochlear Nucleus of Rats via Medial Olivocochlear (MOC) Feedback Circuit

Lesion-induced cochlear damage can result in synaptic outgrowth in the ventral cochlear nucleus (VCN). Tinnitus may be associated with the synaptic outgrowth and hyperactivity in the VCN. However, it remains unclear how hearing loss triggers structural synaptic modifications in the VCN of rats subjected to salicylate-induced tinnitus. To address this issue, we evaluated tinnitus-like behavior in rats after salicylate treatment and compared the amplitude of the distortion product evoked otoacoustic emission (DPOAE) and auditory brainstem response (ABR) between control and treated rats. Moreover, we observed the changes in the synaptic ultrastructure and in the expression levels of growth-associated protein (GAP-43), brain-derived neurotrophic factor (BDNF), the microglial marker Iba-1 and glial fibrillary acidic protein (GFAP) in the VCN. After salicylate treatment (300 mg/kg/day for 4 and 8 days), analysis of the gap prepulse inhibition of the acoustic startle showed that the rats were experiencing tinnitus. The changes in the DPOAE and ABR amplitude indicated an improvement in cochlear sensitivity and a reduction in auditory input following salicylate treatment. The treated rats displayed more synaptic vesicles and longer postsynaptic density in the VCN than the control rats. We observed that the GAP-43 expression, predominantly from medial olivocochlear (MOC) neurons, was significantly up-regulated, and that BDNF- and Iba-1-immunoreactive cells were persistently decreased after salicylate administration. Furthermore, GFAP-immunoreactive astrocytes, which is associated with synaptic regrowth, was significantly increased in the treated groups. Our study revealed that reduced auditory nerve activity triggers synaptic outgrowth and hyperactivity in the VCN via a MOC neural feedback circuit. Structural synaptic modifications may be a reflexive process that compensates for the reduced auditory input after salicylate administration. However, massive increases in excitatory synapses in the VCN may represent a detrimental process that causes central hyperactivity, leading to tinnitus.

Recovery of Dynamics and Function in Spiking Neural Networks with Closed-Loop Control

There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

Brain stimulation is being used to ease symptoms in several neurological disorders in cases where pharmacological treatment is not effective (anymore). The most common way for stimulation so far has been to apply a fixed, predetermined stimulus irrespective of the actual state of the brain or the condition of the patient. Recently, alternative strategies such as event-triggered stimulation protocols have attracted the interest of researchers. In these protocols the state of the affected brain area is continuously monitored, but the stimulus is only applied if certain criteria are met. Here we go one step further and present a truly closed-loop stimulation protocol. That is, a stimulus is being continuously provided and the magnitude of the stimulus depends, at any point in time, on the ongoing neural activity dynamics of the affected brain area. This results not only in suppression of the pathological activity, but also in a partial recovery of the transfer function of the activity dynamics. Thus, the ability of the lesioned brain area to carry out relevant computations is restored up to a point as well.

Closed-loop firing rate regulation of two interacting excitatory and inhibitory neural populations of the basal ganglia

This paper develops a new closed-loop firing rate regulation strategy for a population of neurons in the subthalamic nucleus, derived using a model-based analysis of the basal ganglia. The system is described using a firing rate model, in order to analyse the generation of beta-band oscillations. On this system, a proportional regulation of the firing rate reduces the gain of the subthalamo-pallidal loop in the parkinsonian case, thus impeding pathological oscillation generation. A filter with a well-chosen frequency is added to this proportional scheme, in order to avoid a potential instability of the feedback loop due to actuation and measurement delays. Our main result is a set of conditions on the parameters of the stimulation strategy that guarantee both its stability and a prescribed delay margin. A discussion on the applicability of the proposed method and a complete set of mathematical proofs is included.

Identification of a novel circadian clock modulator controlling BMAL1 expression through a ROR/REV-ERB-response element-dependent mechanism

Circadian rhythms, biological oscillations with a period of about 24 h, are maintained by an innate genetically determined time-keeping system called the molecular circadian clockwork. Despite the physiological and clinical importance of the circadian clock, development of small molecule modulators targeting the core clock machinery has only recently been initiated. BMAL1, a core clock gene, is controlled by a ROR/REV-ERB-response element (RORE)-dependent mechanism, which plays an important role in stabilizing the period of the molecular circadian clock. Therefore, we aimed to identify a novel small molecule modulator that regulates Bmal1 gene expression in RORE-dependency, thereby influencing the molecular feedback loop of the circadian clock. For this purpose, we carried out a cell-based screen of more than 1000 drug-like compounds, using a luciferase reporter driven by the proximal region of the mouse Bmal1 promoter. One compound, designated KK-S6, repressed the RORE-dependent transcriptional activity of the mBmal1 promoter and reduced endogenous BMAL1 protein expression. More importantly, KK-S6 significantly altered the amplitude of circadian oscillations of Bmal1 and Per2 promoter activities in a dose-dependent manner, but barely affected the period length. KK-S6 effectively decreased mRNA expression of metabolic genes acting downstream of REV-ERBα, Pai-1 and Citrate synthase, that contain RORE cis-element in their promoter. KK-S6 likely acts in a RORE-dependent manner by reinforcing the REV-ERBα activity, though not by the same mechanism as known REV-ERB agonists. In conclusion, the present study demonstrates that KK-S6 functions as a novel modulator of the amplitude of molecular circadian rhythms by influencing RORE-mediated BMAL1 expression.

Closed-loop feedback control and bifurcation analysis of epileptiform activity via optogenetic stimulation in a mathematical model of human cortex

Optogenetics provides a method of neuron stimulation that has high spatial, temporal, and cell-type specificity. Here we present a model of optogenetic feedback control that targets the inhibitory population, which expresses light-sensitive channelrhodopsin-2 channels, in a mean-field model of undifferentiated cortex that is driven to seizures. The inhibitory population is illuminated with an intensity that is a function of electrode measurements obtained via the cortical model. We test the efficacy of this control method on seizurelike activity observed in two parameter spaces of the cortical model that most closely correspond to seizures observed in patients. We also compare the effect of closed-loop and open-loop control on seizurelike activity using a less-complicated ordinary differential equation model of the undifferentiated cortex in parameter space. Seizurelike activity is successfully suppressed in both parameter planes using optimal illumination intensities less likely to have adverse effects on cortical tissue.

The Role of Symmetric Stem Cell Divisions in Tissue Homeostasis

Successful maintenance of cellular lineages critically depends on the fate decision dynamics of stem cells (SCs) upon division. There are three possible strategies with respect to SC fate decision symmetry: (a) asymmetric mode, when each and every SC division produces one SC and one non-SC progeny; (b) symmetric mode, when 50% of all divisions produce two SCs and another 50%-two non-SC progeny; (c) mixed mode, when both the asymmetric and two types of symmetric SC divisions co-exist and are partitioned so that long-term net balance of the lineage output stays constant. Theoretically, either of these strategies can achieve lineage homeostasis. However, it remains unclear which strategy(s) are more advantageous and under what specific circumstances, and what minimal control mechanisms are required to operate them. Here we used stochastic modeling to analyze and quantify the ability of different types of divisions to maintain long-term lineage homeostasis, in the context of different control networks. Using the example of a two-component lineage, consisting of SCs and one type of non-SC progeny, we show that its tight homeostatic control is not necessarily associated with purely asymmetric divisions. Through stochastic analysis and simulations we show that asymmetric divisions can either stabilize or destabilize the lineage system, depending on the underlying control network. We further apply our computational model to biological observations in the context of a two-component lineage of mouse epidermis, where autonomous lineage control has been proposed and notable regional differences, in terms of symmetric division ratio, have been noted-higher in thickened epidermis of the paw skin as compared to ear and tail skin. By using our model we propose a possible explanation for the regional differences in epidermal lineage control strategies. We demonstrate how symmetric divisions can work to stabilize paw epidermis lineage, which experiences high level of micro-injuries and a lack of hair follicles as a back-up source of SCs.

Hypothalamic carnitine metabolism integrates nutrient and hormonal feedback to regulate energy homeostasis

The maintenance of energy homeostasis requires the hypothalamic integration of nutrient feedback cues, such as glucose, fatty acids, amino acids, and metabolic hormones such as insulin, leptin and ghrelin. Although hypothalamic neurons are critical to maintain energy homeostasis research efforts have focused on feedback mechanisms in isolation, such as glucose alone, fatty acids alone or single hormones. However this seems rather too simplistic considering the range of nutrient and endocrine changes associated with different metabolic states, such as starvation (negative energy balance) or diet-induced obesity (positive energy balance). In order to understand how neurons integrate multiple nutrient or hormonal signals, we need to identify and examine potential intracellular convergence points or common molecular targets that have the ability to sense glucose, fatty acids, amino acids and hormones. In this review, we focus on the role of carnitine metabolism in neurons regulating energy homeostasis. Hypothalamic carnitine metabolism represents a novel means for neurons to facilitate and control both nutrient and hormonal feedback. In terms of nutrient regulation, carnitine metabolism regulates hypothalamic fatty acid sensing through the actions of CPT1 and has an underappreciated role in glucose sensing since carnitine metabolism also buffers mitochondrial matrix levels of acetyl-CoA, an allosteric inhibitor of pyruvate dehydrogenase and hence glucose metabolism. Studies also show that hypothalamic CPT1 activity also controls hormonal feedback. We hypothesis that hypothalamic carnitine metabolism represents a key molecular target that can concurrently integrate nutrient and hormonal information, which is critical to maintain energy homeostasis. We also suggest this is relevant to broader neuroendocrine research as it predicts that hormonal signaling in the brain varies depending on current nutrient status. Indeed, the metabolic action of ghrelin, leptin or insulin at POMC or NPY neurons may depend on appropriate nutrient-sensing in these neurons and we hypothesize carnitine metabolism is critical in the integrative processing. Future research is required to examine the neuron-specific effects of carnitine metabolism on concurrent nutrient- and hormonal-sensing in AgRP and POMC neurons.

The Rice Transcription Factor WRKY53 Suppresses Herbivore-Induced Defenses by Acting as a Negative Feedback Modulator of Mitogen-Activated Protein Kinase Activity

The mechanisms by which herbivore-attacked plants activate their defenses are well studied. By contrast, little is known about the regulatory mechanisms that allow them to control their defensive investment and avoid a defensive overshoot. We characterized a rice (Oryza sativa) WRKY gene, OsWRKY53, whose expression is rapidly induced upon wounding and induced in a delayed fashion upon attack by the striped stem borer (SSB) Chilo suppressalis. The transcript levels of OsWRKY53 are independent of endogenous jasmonic acid but positively regulated by the mitogen-activated protein kinases OsMPK3/OsMPK6. OsWRKY53 physically interacts with OsMPK3/OsMPK6 and suppresses their activity in vitro. By consequence, it modulates the expression of defensive, MPK-regulated WRKYs and thereby reduces jasmonic acid, jasmonoyl-isoleucine, and ethylene induction. This phytohormonal reconfiguration is associated with a reduction in trypsin protease inhibitor activity and improved SSB performance. OsWRKY53 is also shown to be a negative regulator of plant growth. Taken together, these results show that OsWRKY53 functions as a negative feedback modulator of MPK3/MPK6 and thereby acts as an early suppressor of induced defenses. OsWRKY53 therefore enables rice plants to control the magnitude of their defensive investment during early signaling.

Evolutionary multiobjective design of a flexible caudal fin for robotic fish

Robotic fish accomplish swimming by deforming their bodies or other fin-like appendages. As an emerging class of embedded computing system, robotic fish are anticipated to play an important role in environmental monitoring, inspection of underwater structures, tracking of hazardous wastes and oil spills, and the study of live fish behaviors. While integration of flexible materials (into the fins and/or body) holds the promise of improved swimming performance (in terms of both speed and maneuverability) for these robots, such components also introduce significant design challenges due to the complex material mechanics and hydrodynamic interactions. The problem is further exacerbated by the need for the robots to meet multiple objectives (e.g., both speed and energy efficiency). In this paper, we propose an evolutionary multiobjective optimization approach to the design and control of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to investigate morphological and control parameter values that optimize swimming speed and power usage. Several evolved fin designs are validated experimentally with a small robotic fish, where fins of different stiffness values and sizes are printed with a multi-material 3D printer. Experimental results confirm the effectiveness of the proposed design approach in balancing the two competing objectives.

Neuromechanical model of praying mantis explores the role of descending commands in pre-strike pivots

Praying mantises hunt by standing on their meso- and metathoracic legs and using them to rotate and translate (together, 'pivot') their bodies toward prey. We have developed a neuromechanical software model of the praying mantis Tenodera sinensis to use as a platform for testing postural controllers that the animal may use while hunting. Previous results showed that a feedforward model was insufficient for capturing the diversity of posture observed in the animal (Szczecinski et al 2014 Biomimetic and Biohybrid Syst. 3 296-307). Therefore we have expanded upon this model to make a flexible controller with feedback that more closely mimics the animal. The controller actuates 24 joints in the legs of a dynamical model to orient the head and translate the thorax toward prey. It is controlled by a simulation of nonspiking neurons assembled as a highly simplified version of networks that may exist in the mantid central complex and thoracic ganglia. Because of the distributed nature of these networks, we hypothesize that descending commands that orient the mantis toward prey may be simple direction-of-intent signals, which are turned into motor commands by the structure of low-level networks in the thoracic ganglia. We verify this through a series of experiments with the model. It captures the speed and range of mantid pivots as reported in other work (Yamawaki et al 2011 J. Insect Physiol. 57 1010-6). It is capable of pivoting toward prey from a variety of initial postures, as seen in the animal. Finally, we compare the model's joint kinematics during pivots to preliminary 3D kinematics collected from Tenodera.

Pre-Synaptic Inhibition of Afferent Feedback in the Macaque Spinal Cord Does Not Modulate with Cycles of Peripheral Oscillations Around 10 Hz

Spinal interneurons are partially phase-locked to physiological tremor around 10 Hz. The phase of spinal interneuron activity is approximately opposite to descending drive to motoneurons, leading to partial phase cancellation and tremor reduction. Pre-synaptic inhibition of afferent feedback modulates during voluntary movements, but it is not known whether it tracks more rapid fluctuations in motor output such as during tremor. In this study, dorsal root potentials (DRPs) were recorded from the C8 and T1 roots in two macaque monkeys following intra-spinal micro-stimulation (random inter-stimulus interval 1.5-2.5 s, 30-100 μA), whilst the animals performed an index finger flexion task which elicited peripheral oscillations around 10 Hz. Forty one responses were identified with latency < 5 ms; these were narrow (mean width 0.59 ms), and likely resulted from antidromic activation of afferents following stimulation near terminals. Significant modulation during task performance occurred in 16/41 responses, reflecting terminal excitability changes generated by pre-synaptic inhibition (Wall's excitability test). Stimuli falling during large-amplitude 8-12 Hz oscillations in finger acceleration were extracted, and sub-averages of DRPs constructed for stimuli delivered at different oscillation phases. Although some apparent phase-dependent modulation was seen, this was not above the level expected by chance. We conclude that, although terminal excitability reflecting pre-synaptic inhibition of afferents modulates over the timescale of a voluntary movement, it does not follow more rapid changes in motor output. This suggests that pre-synaptic inhibition is not part of the spinal systems for tremor reduction described previously, and that it plays a role in overall-but not moment-by-moment-regulation of feedback gain.

Synchronization of Asynchronous Switched Boolean Network

In this paper, the complete synchronizations for asynchronous switched Boolean network with free Boolean sequence controllers and close-loop controllers are studied. First, the basic asynchronous switched Boolean network model is provided. With the method of semi-tensor product, the Boolean dynamics is translated into linear representation. Second, necessary and sufficient conditions for ASBN synchronization with free Boolean sequence control and close-loop control are derived, respectively. Third, some illustrative examples are provided to show the efficiency of the proposed methods.

Reinforcement Learning of Linking and Tracing Contours in Recurrent Neural Networks

The processing of a visual stimulus can be subdivided into a number of stages. Upon stimulus presentation there is an early phase of feedforward processing where the visual information is propagated from lower to higher visual areas for the extraction of basic and complex stimulus features. This is followed by a later phase where horizontal connections within areas and feedback connections from higher areas back to lower areas come into play. In this later phase, image elements that are behaviorally relevant are grouped by Gestalt grouping rules and are labeled in the cortex with enhanced neuronal activity (object-based attention in psychology). Recent neurophysiological studies revealed that reward-based learning influences these recurrent grouping processes, but it is not well understood how rewards train recurrent circuits for perceptual organization. This paper examines the mechanisms for reward-based learning of new grouping rules. We derive a learning rule that can explain how rewards influence the information flow through feedforward, horizontal and feedback connections. We illustrate the efficiency with two tasks that have been used to study the neuronal correlates of perceptual organization in early visual cortex. The first task is called contour-integration and demands the integration of collinear contour elements into an elongated curve. We show how reward-based learning causes an enhancement of the representation of the to-be-grouped elements at early levels of a recurrent neural network, just as is observed in the visual cortex of monkeys. The second task is curve-tracing where the aim is to determine the endpoint of an elongated curve composed of connected image elements. If trained with the new learning rule, neural networks learn to propagate enhanced activity over the curve, in accordance with neurophysiological data. We close the paper with a number of model predictions that can be tested in future neurophysiological and computational studies.

Our experience with the visual world allows us to group image elements that belong to the same perceptual object and to segregate them from other objects and the background. If subjects learn to group contour elements, this experience influences neuronal activity in early visual cortical areas, including the primary visual cortex (V1). Learning presumably depends on alterations in the pattern of connections within and between areas of the visual cortex. However, the processes that control changes in connectivity are not well understood. Here we present the first computational model that can train a neural network to integrate collinear contour elements into elongated curves and to trace a curve through the visual field. The new learning algorithm trains fully recurrent neural networks, provided the connectivity causes the networks to reach a stable state. The model reproduces the behavioral performance of monkeys trained in these tasks and explains the patterns of neuronal activity in the visual cortex that emerge during learning, which is remarkable because the only feedback for the model is a reward for successful trials. We discuss a number of the model predictions that can be tested in future neuroscientific work.

Distributed flow sensing for closed-loop speed control of a flexible fish robot

Flexibility plays an important role in fish behavior by enabling high maneuverability for predator avoidance and swimming in turbulent flow. This paper presents a novel flexible fish robot equipped with distributed pressure sensors for flow sensing. The body of the robot is molded from soft, hyperelastic material, which provides flexibility. Its Joukowski-foil shape is conducive to modeling the fluid analytically. A quasi-steady potential-flow model is adopted for real-time flow estimation, whereas a discrete-time vortex-shedding flow model is used for higher-fidelity simulation. The dynamics for the flexible fish robot yield a reduced model for one-dimensional swimming. A recursive Bayesian filter assimilates pressure measurements to estimate flow speed, angle of attack, and foil camber. The closed-loop speed-control strategy combines an inverse-mapping feedforward controller based on an average model derived for periodic actuation of angle-of-attack and a proportional-integral feedback controller utilizing the estimated flow information. Simulation and experimental results are presented to show the effectiveness of the estimation and control strategy. The paper provides a systematic approach to distributed flow sensing for closed-loop speed control of a flexible fish robot by regulating the flapping amplitude.

Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit

Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in many brain systems. Despite difference in the anatomy or functions of neuronal circuits containing inhibition, two basic motifs repeatedly emerge: feed-forward and feedback. In the locust, it was proposed that a subset of lateral horn interneurons (LHNs), provide feed-forward inhibition onto Kenyon cells (KCs) to maintain their sparse firing—a property critical for olfactory learning and memory. But recently it was established that a single inhibitory cell, the giant GABAergic neuron (GGN), is the main and perhaps sole source of inhibition in the mushroom body, and that inhibition from this cell is mediated by a feedback (FB) loop including KCs and the GGN. To clarify basic differences in the effects of feedback vs. feed-forward inhibition in circuit dynamics we here use a model of the locust olfactory system. We found both inhibitory motifs were able to maintain sparse KCs responses and provide optimal odor discrimination. However, we further found that only FB inhibition could create a phase response consistent with data recorded in vivo. These findings describe general rules for feed-forward versus feedback inhibition and suggest GGN is potentially capable of providing the primary source of inhibition to the KCs. A better understanding of how inhibitory motifs impact post-synaptic neuronal activity could be used to reveal unknown inhibitory structures within biological networks.

Understanding how inhibitory neurons interact with excitatory neurons is critical for understanding the behaviors of neuronal networks. Here we address this question with simple but biologically relevant models based on the anatomy of the locust olfactory pathway. Two ubiquitous and basic inhibitory motifs were tested: feed-forward and feedback. Feed-forward inhibition typically occurs between different brain areas when excitatory neurons excite inhibitory cells, which then inhibit a group of postsynaptic excitatory neurons outside of the initializing excitatory neurons’ area. On the other hand, the feedback inhibitory motif requires a population of excitatory neurons to drive the inhibitory cells, which in turn inhibit the same population of excitatory cells. We found the type of the inhibitory motif determined the timing with which each group of cells fired action potentials in comparison to one another (relative timing). It also affected the range of inhibitory neurons’ activity, with the inhibitory neurons having a wider range in the feedback circuit than that in the feed-forward one. These results will allow predicting the type of the connectivity structure within unexplored biological circuits given only electrophysiological recordings.

Antagonistic Coevolution Drives Whack-a-Mole Sensitivity in Gene Regulatory Networks

Robustness, defined as tolerance to perturbations such as mutations and environmental fluctuations, is pervasive in biological systems. However, robustness often coexists with its counterpart, evolvability--the ability of perturbations to generate new phenotypes. Previous models of gene regulatory network evolution have shown that robustness evolves under stabilizing selection, but it is unclear how robustness and evolvability will emerge in common coevolutionary scenarios. We consider a two-species model of coevolution involving one host and one parasite population. By using two interacting species, key model parameters that determine the fitness landscapes become emergent properties of the model, avoiding the need to impose these parameters externally. In our study, parasites are modeled on species such as cuckoos where mimicry of the host phenotype confers high fitness to the parasite but lower fitness to the host. Here, frequent phenotype changes are favored as each population continually adapts to the other population. Sensitivity evolves at the network level such that point mutations can induce large phenotype changes. Crucially, the sensitive points of the network are broadly distributed throughout the network and continually relocate. Each time sensitive points in the network are mutated, new ones appear to take their place. We have therefore named this phenomenon "whack-a-mole" sensitivity, after a popular fun park game. We predict that this type of sensitivity will evolve under conditions of strong directional selection, an observation that helps interpret existing experimental evidence, for example, during the emergence of bacterial antibiotic resistance.

Extracting motor synergies from random movements for low-dimensional task-space control of musculoskeletal robots

In the field of human motor control, the motor synergy hypothesis explains how humans simplify body control dimensionality by coordinating groups of muscles, called motor synergies, instead of controlling muscles independently. In most applications of motor synergies to low-dimensional control in robotics, motor synergies are extracted from given optimal control signals. In this paper, we address the problems of how to extract motor synergies without optimal data given, and how to apply motor synergies to achieve low-dimensional task-space tracking control of a human-like robotic arm actuated by redundant muscles, without prior knowledge of the robot. We propose to extract motor synergies from a subset of randomly generated reaching-like movement data. The essence is to first approximate the corresponding optimal control signals, using estimations of the robot's forward dynamics, and to extract the motor synergies subsequently. In order to avoid modeling difficulties, a learning-based control approach is adopted such that control is accomplished via estimations of the robot's inverse dynamics. We present a kernel-based regression formulation to estimate the forward and the inverse dynamics, and a sliding controller in order to cope with estimation error. Numerical evaluations show that the proposed method enables extraction of motor synergies for low-dimensional task-space control.

An Adapting Auditory-motor Feedback Loop Can Contribute to Generating Vocal Repetition

Consecutive repetition of actions is common in behavioral sequences. Although integration of sensory feedback with internal motor programs is important for sequence generation, if and how feedback contributes to repetitive actions is poorly understood. Here we study how auditory feedback contributes to generating repetitive syllable sequences in songbirds. We propose that auditory signals provide positive feedback to ongoing motor commands, but this influence decays as feedback weakens from response adaptation during syllable repetitions. Computational models show that this mechanism explains repeat distributions observed in Bengalese finch song. We experimentally confirmed two predictions of this mechanism in Bengalese finches: removal of auditory feedback by deafening reduces syllable repetitions; and neural responses to auditory playback of repeated syllable sequences gradually adapt in sensory-motor nucleus HVC. Together, our results implicate a positive auditory-feedback loop with adaptation in generating repetitive vocalizations, and suggest sensory adaptation is important for feedback control of motor sequences.

Repetitions are common in animal vocalizations. Songs of many songbirds contain syllables that repeat a variable number of times, with non-Markovian distributions of repeat counts. The neural mechanism underlying such syllable repetitions is unknown. In this work, we show that auditory feedback plays an important role in sustaining syllable repetitions in the Bengalese finch. Deafening reduces syllable repetitions and skews the repeat number distribution towards short repeats. These effects are explained with our computational model, which suggests that syllable repeats are initially sustained by auditory feedback to the neural networks that drive the syllable production. The feedback strength weakens as the syllable repeats, increasing the likelihood that the syllable repetition stops. Neural recordings confirm such adaptation of auditory feedback to the auditory-motor circuit in the Bengalese finch. Our results suggests that sensory feedback can directly impact repetitions in motor sequences, and may provide insights into neural mechanisms of speech disorders such as stuttering.

A minimum attention control law for ball catching

Digital implementations of control laws typically involve discretization with respect to both time and space, and a control law that can achieve a task at coarser levels of discretization can be said to require less control attention, and also reduced implementation costs. One means of quantitatively capturing the attention of a control law is to measure the rate of change of the control with respect to changes in state and time. In this paper we present an attention-minimizing control law for ball catching and other target tracking tasks based on Brockett's attention criterion. We first highlight the connections between this attention criterion and some well-known principles from human motor control. Under the assumption that the optimal control law is the sum of a linear time-varying feedback term and a time-varying feedforward term, we derive an LQR-based minimum attention tracking control law that is stable, and obtained efficiently via a finite-dimensional optimization over the symmetric positive-definite matrices. Taking ball catching as our primary task, we perform numerical experiments comparing the performance of the various control strategies examined in the paper. Consistent with prevailing theories about human ball catching, our results exhibit several familiar features, e.g., the transition from open-loop to closed-loop control during the catching movement, and improved robustness to spatiotemporal discretization. The presented control laws are applicable to more general tracking problems that are subject to limited communication resources.

Histone monoubiquitination by Clock-Bmal1 complex marks Per1 and Per2 genes for circadian feedback

Circadian rhythms in mammals are driven by a feedback loop in which the transcription factor Clock-Bmal1 activates expression of Per and Cry proteins, which together form a large nuclear complex (Per complex) that represses Clock-Bmal1 activity. We found that mouse Clock-Bmal1 recruits the Ddb1-Cullin-4 ubiquitin ligase to Per (Per1 and Per2), Cry (Cry1 and Cry2) and other circadian target genes. Histone H2B monoubiquitination at Per genes was rhythmic and depended on Bmal1, Ddb1 and Cullin-4a. Depletion of Ddb1-Cullin-4a or an independent decrease in H2B monoubiquitination caused defective circadian feedback and decreased the association of the Per complex with DNA-bound Clock-Bmal1. Clock-Bmal1 thus covalently marks Per genes for subsequent recruitment of the Per complex. Our results reveal a chromatin-mediated signal from the positive to the negative limb of the clock that provides a licensing mechanism for circadian feedback.

Universal protein distributions in a model of cell growth and division

Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means. For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division across generations. Here we present a stochastic growth-and-division model with feedback which captures these observed properties. The limiting copy number distribution is calculated exactly, and a single parameter is found to determine the distribution shape and the variance-to-mean relation. Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy and leads to predictions for future experiments.

Shear stress blunts tubuloglomerular feedback partially mediated by primary cilia and nitric oxide at the macula densa

The present study tested whether primary cilia on macula densa serve as a flow sensor to enhance nitric oxide synthase 1 (NOS1) activity and inhibit tubuloglomerular feedback (TGF). Isolated perfused macula densa was loaded with calcein red and 4,5-diaminofluorescein diacetate to monitor cell volume and nitric oxide (NO) generation. An increase in tubular flow rate from 0 to 40 nl/min enhanced NO production by 40.0 ± 1.2%. The flow-induced NO generation was blocked by an inhibitor of NOS1 but not by inhibition of the Na/K/2Cl cotransporter or the removal of electrolytes from the perfusate. NO generation increased from 174.8 ± 21 to 276.1 ± 24 units/min in cultured MMDD1 cells when shear stress was increased from 0.5 to 5.0 dynes/cm(2). The shear stress-induced NO generation was abolished in MMDD1 cells in which the cilia were disrupted using a siRNA to ift88. Increasing the NaCl concentration of the tubular perfusate from 10 to 80 mM NaCl in the isolated perfused juxtaglomerular preparation reduced the diameter of the afferent arteriole by 3.8 ± 0.1 μm. This response was significantly blunted to 2.5 ± 0.2 μm when dextran was added to the perfusate to increase the viscosity and shear stress. Inhibition of NOS1 blocked the effect of dextran on TGF response. In vitro, the effects of raising perfusate viscosity with dextran on tubular hydraulic pressure were minimized by reducing the outflow resistance to avoid stretching of tubular cells. These results suggest that shear stress stimulates primary cilia on the macula densa to enhance NO generation and inhibit TGF responsiveness.

Global change feed-back inhibits cyanobacterial photosynthesis

Cyanobacteria are an important component of aquatic ecosystems, with a proliferation of massive cyanobacterial blooms predicted worldwide under increasing warming conditions. In addition to temperature, other global change related variables, such as water column stratification, increases in dissolved organic matter (DOM) discharge into freshwater systems and greater wind stress (i.e., more opaque and mixed upper water column/epilimnion) might also affect the responses of cyanobacteria. However, the combined effects of these variables on cyanobacterial photosynthesis remain virtually unknown. Here we present evidence that this combination of global-change conditions results in a feed-back mechanism by which, fluctuations in solar ultraviolet radiation (UVR, 280-400 nm) due to vertical mixing within the epilimnion act synergistically with increased DOM to impair cyanobacterial photosynthesis as the water column progressively darkens. The main consequence of such a feed-back response is that these organisms will not develop large blooms in areas of latitudes higher than 30°, in both the Northern and Southern Hemispheres, where DOM inputs and surface wind stress are increasing.

Hovering and forward flight of the hawkmoth Manduca sexta: trim search and 6-DOF dynamic stability characterization

We show that the forward flight speed affects the stability characteristics of the longitudinal and lateral dynamics of a flying hawkmoth; dynamic modal structures of both the planes of motion are altered due to variations in the stability derivatives. The forward flight speed u e is changed from 0.00 to 1.00 m s(-1) with an increment of 0.25 m s(-1). (The equivalent advance ratio is 0.00 to 0.38; the advance ratio is the ratio of the forward flight speed to the average wing tip speed.) As the flight speed increases, for the longitudinal dynamics, an unstable oscillatory mode becomes more unstable. Also, we show that the up/down (w(b)) dynamics become more significant at a faster flight speed due to the prominent increase in the stability derivative Z(u) (up/down force due to the forward/backward velocity). For the lateral dynamics, the decrease in the stability derivative L(v) (roll moment due to side slip velocity) at a faster flight speed affects a slightly damped stable oscillatory mode, causing it to become more stable; however, the t(half) (the time taken to reach half the amplitude) of this slightly damped stable oscillatory mode remains relatively long (∼12T at u(e) = 1 m s(-1); T is wingbeat period) compared to the other modes of motion, meaning that this mode represents the most vulnerable dynamics among the lateral dynamics at all flight speeds. To obtain the stability derivatives, trim conditions for linearization are numerically searched to find the exact trim trajectory and wing kinematics using an algorithm that uses the gradient information of a control effectiveness matrix and fully coupled six-degrees of freedom nonlinear multibody equations of motion. With this algorithm, trim conditions that consider the coupling between the dynamics and aerodynamics can be obtained. The body and wing morphology, and the wing kinematics used in this study are based on actual measurement data from the relevant literature. The aerodynamic model of the flapping wings of a hawkmoth is based on the blade element theory, and the necessary aerodynamic coefficients, including the lift, drag and wing pitching moment, are experimentally obtained from the results of previous work by the authors.

A biologically based neural system coordinates the joints and legs of a tetrapod

A biologically inspired neural control system has been developed that coordinates a tetrapod trotting gait in the sagittal plane. The developed neuromechanical system is used to explore properties of connections in inter-leg and intra-leg coordination. The neural controller is built with biologically based neurons and synapses, and connections are based on data from literature where available. It is applied to a planar biomechanical model of a rat with 14 joints, each actuated by a pair of antagonistic Hill muscle models. The controller generates tension in the muscles through activation of simulated motoneurons. The hind leg and inter-leg control networks are based on pathways discovered in cat research tuned to the kinematic motions of a rat. The foreleg network was developed by extrapolating analogous pathways from the hind legs. The formulated intra-leg and inter-leg networks properly coordinate the joints and produce motions similar to those of a walking rat. Changing the strength of a single inter-leg connection is sufficient to account for differences in phase timing in different trotting rats.

Attenuation of the oestrogen positive feedback mechanism with the age in postmenopausal women

OBJECTIVE

It has been reported that the positive feedback mechanism of oestrogens and progesterone is preserved, although attenuated, in late postmenopausal years. Whether this is also true for the positive feedback effect of oestrogens alone has not been investigated.

DESIGN

Prospective intervention study.

PATIENTS

Thirty healthy postmenopausal women.

MEASUREMENTS

The women were divided into three groups according to the years since menopause (group I: 2-8 years, group II: 9-17 years, group III: 18-25 years). They were studied during a period of 41 days. Two acute experiments (EP) of exogenous oestradiol, given via skin patches, were performed from days 1 to 7 (EP1) and from days 35 to 41 (EP2) to induce an LH surge. Between the two experiments (days 7-34), oestradiol was given at the dose of 100 μg every 3 days, while oral progesterone was added from day 21 to day 34 in order to simulate a luteal phase. Blood samples were taken every 6 h during EP1 and EP2 as well as on days 8, 13, 20, 21, 27 and 34. FSH, LH, oestradiol and progesterone were measured in all blood samples.

RESULTS

An LH surge occurred as a result of the oestradiol positive feedback mechanism in group I and in group II, in both EP1 and EP2. Peak LH values during the surge were significantly lower in group II than in group I in both experiments. None of the patients in group III displayed an LH surge.

CONCLUSIONS

These results demonstrate for the first time a gradual attenuation of the pituitary response to oestrogenic provocation over a certain period following the menopause, with complete abolition after 20 years. It is suggested that the reserves of pituitary gonadotrophs diminish with age.

The unified ICE-CBF pathway provides a transcriptional feedback control of freezing tolerance during cold acclimation in Arabidopsis

During cold acclimation, C-repeat binding factors (CBFs) activate downstream targets, such as cold-regulated genes, leading to the acquisition of freezing tolerance in plants. Inducer of CBF expression 1 (ICE1) plays a key role by activating CBF3 expression in shaping the cold-induced transcriptome. While the ICE1-CBF3 regulon constitutes a major cold acclimation pathway, gene regulatory networks governing the CBF signaling are poorly understood. Here, we demonstrated that ICE1 and its paralog ICE2 induce CBF1, CBF2, and CBF3 by binding to the gene promoters. ICE2, like ICE1, was ubiquitinated by the high expression of osmotically responsive gene 1 (HOS1) E3 ubiquitin ligase. Whereas ICE2-defective ice2-2 mutant did not exhibit any discernible freezing-sensitive phenotypes, ice1-2 ice2-2/+ plant, which is defective in ICE1 and has a heterozygotic ice2 mutation, exhibited significantly reduced freezing tolerance. Accordingly, all three CBF genes were markedly down-regulated in the ice1-2 ice2-2/+ plant, indicating that ICE1 and ICE2 are functionally redundant with different implementations in inducing CBF genes. Together with the negative regulation of CBF3 by CBF2, we propose that the unified ICE-CBF pathway provides a transcriptional feedback of freezing tolerance to sustain plant development and survival during cold acclimation.

Self-organized dynamical complexity in human wakefulness and sleep: different critical brain-activity feedback for conscious and unconscious states

Criticality reportedly describes brain dynamics. The main critical feature is the presence of scale-free neural avalanches, whose auto-organization is determined by a critical branching ratio of neural-excitation spreading. Other features, directly associated to second-order phase transitions, are: (i) scale-free-network topology of functional connectivity, stemming from suprathreshold pairwise correlations, superimposable, in waking brain activity, with that of ferromagnets at Curie temperature; (ii) temporal long-range memory associated to renewal intermittency driven by abrupt fluctuations in the order parameters, detectable in human brain via spatially distributed phase or amplitude changes in EEG activity. Herein we study intermittent events, extracted from 29 night EEG recordings, including presleep wakefulness and all phases of sleep, where different levels of mentation and consciousness are present. We show that while critical avalanching is unchanged, at least qualitatively, intermittency and functional connectivity, present during conscious phases (wakefulness and REM sleep), break down during both shallow and deep non-REM sleep. We provide a theory for fragmentation-induced intermittency breakdown and suggest that the main difference between conscious and unconscious states resides in the backwards causation, namely on the constraints that the emerging properties at large scale induce to the lower scales. In particular, while in conscious states this backwards causation induces a critical slowing down, preserving spatiotemporal correlations, in dreamless sleep we see a self-organized maintenance of moduli working in parallel. Critical avalanches are still present, and establish transient auto-organization, whose enhanced fluctuations are able to trigger sleep-protecting mechanisms that reinstate parallel activity. The plausible role of critical avalanches in dreamless sleep is to provide a rapid recovery of consciousness, if stimuli are highly arousing.

Regulation of lipid metabolism in the green microalga Chlorella protothecoides by heterotrophy-photoinduction cultivation regime

Proteomics in conjunction with biochemical strategy was employed to unravel regulation of lipid metabolism in the green microalga Chlorella protothecoides by heterotrophy-photoinduction cultivation regime (HPC). Interestingly, HPC triggered transiently synthesis of starch followed by substantial lipid accumulation. And a marked decrease in intracellular protein and chlorophyll contents was also observed after 12h of photo-induction. The highest lipid content of 50.5% was achieved upon the photo-induction stage, which represented 69.3% higher than that of the end of heterotrophic cultivation. Results suggested that turnover of carbon-nitrogen-rich compounds such as starch, protein, and chlorophyll might provide carbon or energy for lipid accumulation. The proteomics analysis indicated that several pathways including glycolysis, TCA cycle, β-oxidation of fatty acids, Calvin cycle, photosynthesis, energy and transport, protein biosynthesis, regulate and defense were involved in the lipid biosynthesis. Malate dehydrogenase and acyl-CoA dehydrogenase were suggested as key regulatory factors in enhancing lipid accumulation.

Feedback compensation of intrinsic muscle properties during torque regulation tasks

Many functional tasks require regulating appropriate forces or torques even under unpredictable disturbances. However, how this regulation can be achieved remains poorly understood. Limb impedance describes the relationship between externally imposed displacements to the limb and the changes in force or torque generated in response. Low limb impedance is preferred during torque regulation tasks. However, low-frequency impedance increases with muscle activation, which is counterproductive to torque regulation. The purpose of this study was to quantify the ability to voluntarily reduce limb impedance during torque regulation tasks, and to assess if the observed performance is near optimal given the challenges posed by activation-dependent muscle properties and time delays in the neuromuscular system. By examining elbow impedance measured in experiments and predicted by a biomechanical model with an optimal controller, our results demonstrated that individuals can reduce the low-frequency components (below 1 Hz) of elbow impedance during forceful contractions, and that this performance is similar to those predicted by an optimal feedback controller. These findings suggest that neural feedback can compensate for intrinsic muscle properties in a near-optimal manner, thereby allowing torque to be regulated at frequencies below ~ 1 Hz.

Minimal feedback to a rhythm generator improves the robustness to slope variations of a compass biped

In recent years there has been a growing interest in the field of dynamic walking and bio-inspired robots. However, while walking and running on a flat surface have been studied extensively, walking dynamically over terrains with varying slope remains a challenge. Previously we developed an open loop controller based on a central pattern generator (CPG). The controller applied predefined torque patterns to a compass-gait biped, and achieved stable gaits over a limited range of slopes. In this work, this range is greatly extended by applying a once per cycle feedback to the CPG controller. The terrain's slope is measured and used to modify both the CPG frequency and the torque amplitude once per step. A multi-objective optimization algorithm was used to tune the controller parameters for a simulated CB model. The resulting controller successfully traverses terrains with slopes ranging from +7° to -8°, comparable to most slopes found in human constructed environments. Gait stability was verified by computing the linearized Poincaré Map both numerically and analytically.

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.

An animal exploring a natural scene receives sensory inputs that vary, rapidly, over many orders of magnitude. Neurons must transmit these inputs faithfully despite both their limited dynamic range and relatively slow adaptation time scales. One well-accepted strategy for transmitting signals through limited dynamic range channels–predictive coding–transmits only components of the signal that cannot be predicted from the past. Predictive coding algorithms respond maximally to unexpected inputs, making them appealing in describing sensory transmission. However, recent experimental evidence has shown that neuronal circuits adapt quickly, to respond optimally following rapid input changes. Here, we reconcile the predictive coding algorithm with this automatic adaptation, by introducing a fixed nonlinearity into a predictive coding circuit. The resulting network automatically “adapts” its linearized response to different inputs. Indeed, it approximates the performance of an optimal linear circuit implementing predictive coding, without having to vary its internal parameters. Further, adding this nonlinearity to the predictive coding circuit still allows the input to be compressed losslessly, allowing for additional downstream manipulations. Finally, we demonstrate that the nonlinear circuit dynamics match responses in both auditory and visual neurons. Therefore, we believe that this nonlinear circuit may be a general circuit motif that can be applied in different neural circuits, whenever it is necessary to provide an automatic improvement in the quality of the transmitted signal, for a fast varying input distribution.

Energy evaluation of a bio-inspired gait modulation method for quadrupedal locomotion

We have proposed a bio-inspired gait modulation method, by means of which a simulated quadruped model can successfully perform smooth, autonomous gait transitions from a walk to a trot to a gallop, as observed in animals. The model is equipped with a rhythm generator called a central pattern generator (CPG) for each leg. The lateral neighbouring CPGs are mutually and inhibitorily coupled, and the CPG network is hardwired to produce a trot. Adding only the simple feedback of body tilt to each CPG, which was based on input from the postural reflex, led to the emergence of un-programmed walking and galloping at low and high speeds, respectively. Although this autonomous gait transition was a consequence of postural adaptation, it coincidentally also resulted in the minimization of energy consumption, as observed in real animals. In simulations at a variety of constant speeds the energy cost was lower for walking at low speeds and for galloping at high speeds than it was for trotting. Moreover, each gait transition occurred at the optimal speed, such that the model minimised its energy consumption. Thus, gait transitions in simulations that included the bio-inspired gait modulation method were similar to those observed in animals, even from the perspective of energy consumption. This method should therefore be a preferred choice for motion generation and control in biomimetic quadrupedal locomotion.

60 YEARS OF NEUROENDOCRINOLOGY: MEMOIR: Harris' neuroendocrine revolution: of portal vessels and self-priming

Geoffrey Harris, while still a medical student at Cambridge, was the first researcher (1937) to provide experimental proof for the then tentative view that the anterior pituitary gland was controlled by the CNS. The elegant studies carried out by Harris in the 1940s and early 1950s, alone and in collaboration with John Green and Dora Jacobsohn, established that this control was mediated by a neurohumoral mechanism that involved the transport by hypophysial portal vessel blood of chemical substances from the hypothalamus to the anterior pituitary gland. The neurohumoral control of anterior pituitary secretion was proved by the isolation and characterisation of the 'chemical substances' (mainly neuropeptides) and the finding that these substances were released into hypophysial portal blood in a manner consistent with their physiological functions. The new discipline of neuroendocrinology - the way that the brain controls endocrine glands and vice versa - revolutionised the treatment of endocrine disorders such as growth and pubertal abnormalities, infertility and hormone-dependent tumours, and it underpins our understanding of the sexual differentiation of the brain and key aspects of behaviour and mental disorder. Neuroendocrine principles are illustrated in this Thematic Review by way of Harris' major interest: hypothalamic-pituitary-gonadal control. Attention is focussed on the measurement of GnRH in hypophysial portal blood and the role played by the self-priming effect of GnRH in promoting the onset of puberty and enabling the oestrogen-induced surge or pulses of GnRH to trigger the ovulatory gonadotrophin surge in humans and other spontaneously ovulating mammals.

Interplay between stochasticity and negative feedback leads to pulsed dynamics and distinct gene activity patterns

Gene expression is an inherently stochastic process that depends on the structure of the biochemical regulatory network in which the gene is embedded. Here we study the dynamical consequences of the interplay between stochastic gene switching and the widespread negative feedback regulatory loop in a simple model of a biochemical regulatory network. Using a simplified hybrid simulation approach, in which only the gene activation is modeled stochastically, we find that stochasticity in gene switching by itself can induce pulses in the system, providing also analytical insights into their origin. Furthermore, we find that this simple network is able to reproduce both exponential and peaked distributions of gene active and inactive times similar to those that have been observed experimentally. This simplified hybrid simulation approach also allows us to link these patterns to the dynamics of the system for each gene state.

60 YEARS OF NEUROENDOCRINOLOGY: The hypothalamo-prolactin axis

The hypothalamic control of prolactin secretion is different from other anterior pituitary hormones, in that it is predominantly inhibitory, by means of dopamine from the tuberoinfundibular dopamine neurons. In addition, prolactin does not have an endocrine target tissue, and therefore lacks the classical feedback pathway to regulate its secretion. Instead, it is regulated by short loop feedback, whereby prolactin itself acts in the brain to stimulate production of dopamine and thereby inhibit its own secretion. Finally, despite its relatively simple name, prolactin has a broad range of functions in the body, in addition to its defining role in promoting lactation. As such, the hypothalamo-prolactin axis has many characteristics that are quite distinct from other hypothalamo-pituitary systems. This review will provide a brief overview of our current understanding of the neuroendocrine control of prolactin secretion, in particular focusing on the plasticity evident in this system, which keeps prolactin secretion at low levels most of the time, but enables extended periods of hyperprolactinemia when necessary for lactation. Key prolactin functions beyond milk production will be discussed, particularly focusing on the role of prolactin in inducing adaptive responses in multiple different systems to facilitate lactation, and the consequences if prolactin action is impaired. A feature of this pleiotropic activity is that functions that may be adaptive in the lactating state might be maladaptive if prolactin levels are elevated inappropriately. Overall, my goal is to give a flavour of both the history and current state of the field of prolactin neuroendocrinology, and identify some exciting new areas of research development.

60 YEARS OF NEUROENDOCRINOLOGY: Glucocorticoid dynamics: insights from mathematical, experimental and clinical studies

A pulsatile pattern of secretion is a characteristic of many hormonal systems, including the glucocorticoid-producing hypothalamic-pituitary-adrenal (HPA) axis. Despite recent evidence supporting its importance for behavioral, neuroendocrine and transcriptional effects of glucocorticoids, there has been a paucity of information regarding the origin of glucocorticoid pulsatility. In this review we discuss the mechanisms regulating pulsatile dynamics of the HPA axis, and how these dynamics become disrupted in disease. Our recent mathematical, experimental and clinical studies show that glucocorticoid pulsatility can be generated and maintained by dynamic processes at the level of the pituitary-adrenal axis, and that an intra-adrenal negative feedback may contribute to these dynamics.

Transcriptional feedback regulation of YUCCA genes in response to auxin levels in Arabidopsis

The IPyA pathway, the major auxin biosynthesis pathway, is transcriptionally regulated through a negative feedback mechanism in response to active auxin levels. The phytohormone auxin plays an important role in plant growth and development, and levels of active free auxin are determined by biosynthesis, conjugation, and polar transport. Unlike conjugation and polar transport, little is known regarding the regulatory mechanism of auxin biosynthesis. We discovered that expression of genes encoding indole-3-pyruvic acid (IPyA) pathway enzymes is regulated by elevated or reduced active auxin levels. Expression levels of TAR2, YUC1, YUC2, YUC4, and YUC6 were downregulated in response to synthetic auxins [1-naphthaleneacetic acid (NAA) and 2,4-dichlorophenoxyacetic acid (2,4-D)] exogenously applied to Arabidopsis thaliana L. seedlings. Concomitantly, reduced levels of endogenous indole-3-acetic acid (IAA) were observed. Alternatively, expression of these YUCCA genes was upregulated by the auxin biosynthetic inhibitor kynurenine in Arabidopsis seedlings, accompanied by reduced IAA levels. These results indicate that expression of YUCCA genes is regulated by active auxin levels. Similar results were also observed in auxin-overproduction and auxin-deficient mutants. Exogenous application of IPyA to Arabidopsis seedlings preincubated with kynurenine increased endogenous IAA levels, while preincubation with 2,4-D reduced endogenous IAA levels compared to seedlings exposed only to IPyA. These results suggest that in vivo conversion of IPyA to IAA was enhanced under reduced auxin levels, while IPyA to IAA conversion was depressed in the presence of excess auxin. Based on these results, we propose that the IPyA pathway is transcriptionally regulated through a negative feedback mechanism in response to active auxin levels.

Corp Regulates P53 in Drosophila melanogaster via a Negative Feedback Loop

The tumor suppressor P53 is a critical mediator of the apoptotic response to DNA double-strand breaks through the transcriptional activation of pro-apoptotic genes. This mechanism is evolutionarily conserved from mammals to lower invertebrates, including Drosophila melanogaster. P53 also transcriptionally induces its primary negative regulator, Mdm2, which has not been found in Drosophila. In this study we identified the Drosophila gene companion of reaper (corp) as a gene whose overexpression promotes survival of cells with DNA damage in the soma but reduces their survival in the germline. These disparate effects are shared by p53 mutants, suggesting that Corp may be a negative regulator of P53. Confirming this supposition, we found that corp negatively regulates P53 protein level. It has been previously shown that P53 transcriptionally activates corp; thus, Corp produces a negative feedback loop on P53. We further found that Drosophila Corp shares a protein motif with vertebrate Mdm2 in a region that mediates the Mdm2:P53 physical interaction. In Corp, this motif mediates physical interaction with Drosophila P53. Our findings implicate Corp as a functional analog of vertebrate Mdm2 in flies.

Lights on for the molecular players of presynaptic plasticity

Neuronal response or adaption to a changing environment relies on the modulation of synaptic function. How this modulation is achieved remains controversial. In this issue of Neuron, Sugie et al. (2015) now report that active zones of Drosophila photoreceptors undergo activity-dependent changes in their molecular composition.

Thermal pretreatment of olive mill wastewater for efficient methane production: control of aromatic substances degradation by monitoring cyclohexane carboxylic acid

Anaerobic digestion is investigated as a sustainable depurative strategy of olive oil mill wastewater (OOMW). The effect of thermal pretreatment on the anaerobic biodegradation of aromatic compounds present in (OMWW) was investigated. The anaerobic degradation of phenolic compounds, well known to be the main concern related to this kind of effluents, was monitored in batch anaerobic tests at a laboratory scale on samples pretreated at mild (80±1 °C), intermediate (90±1 °C) and high temperature (120±1 °C). The obtained results showed an increase of 34% in specific methane production (SMP) for OMWW treated at the lowest temperature and a decrease of 18% for treatment at the highest temperature. These results were related to the different decomposition pathways of the lignocellulosic compounds obtained in the tested conditions. The decomposition pathway was determined by measuring the concentrations of volatile organic acids, phenols, and chemical oxygen demand (COD) versus time. Cyclohexane carboxylic acid (CHCA) production was identified in all the tests with a maximum concentration of around 200 µmol L(-1) in accordance with the phenols degradation, suggesting that anaerobic digestion of aromatic compounds follows the benzoyl-CoA pathway. Accurate monitoring of this compound was proposed as the key element to control the process evolution. The total phenols (TP) and total COD removals were, with SMP, the highest (TP 62.7%-COD 63.2%) at 80 °C and lowest (TP 44.9%-COD 32.2%) at 120 °C. In all cases, thermal pretreatment was able to enhance the TP removal ability (up to 42% increase).

Asynchronous Rate Chaos in Spiking Neuronal Circuits

The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.

Controlling legs for locomotion-insights from robotics and neurobiology

Walking is the most common terrestrial form of locomotion in animals. Its great versatility and flexibility has led to many attempts at building walking machines with similar capabilities. The control of walking is an active research area both in neurobiology and robotics, with a large and growing body of work. This paper gives an overview of the current knowledge on the control of legged locomotion in animals and machines and attempts to give walking control researchers from biology and robotics an overview of the current knowledge in both fields. We try to summarize the knowledge on the neurobiological basis of walking control in animals, emphasizing common principles seen in different species. In a section on walking robots, we review common approaches to walking controller design with a slight emphasis on biped walking control. We show where parallels between robotic and neurobiological walking controllers exist and how robotics and biology may benefit from each other. Finally, we discuss where research in the two fields diverges and suggest ways to bridge these gaps.

Efferent Feedback in a Spinal-Like Controller: Reaching With Perturbations

We use simulations of a controller that adopts a spinal-like network topology for goal-oriented reaching and assess its sensitivity to the dynamics of internal elements that allow context-independent performance. Such internal elements are often referred to as inverse or forward models of the periphery dynamics, depending on the proposed controller theory. Here, the "models" are used in a forward implementation, and we evaluate how the controller's performance would be affected by the nature of the model. For each point-to-point reaching motion experiment, we use forms of internal "efference models" (e.g., full mathematical representations of peripheral dynamics, simple spindle feedback, etc.) driven by motor reafference, then compare hand trajectories and hand path speeds in the presence or absence of external perturbations. It is demonstrated that a simple velocity-based model reduced the effects of dynamic perturbations by as much as 66%. In addition, the 2D hand trajectories varied from a biological reference by only 0.05 cm. Thus, the controller facilitated biological like motions while providing response to dynamic events which are omitted in earlier biomimetic controllers. This research suggests that these spinal-like systems are robust and tunable via gain-fields without the need of context dependent pre-planning.

Alternative cell polarity behaviours arise from changes in G-protein spatial dynamics

Yeast cells form a single mating projection when exposed to mating pheromone, a classic example of cell polarity. Prolonged treatment with pheromone or specific mutations results in alternative cell polarity behaviours. The authors performed mathematical modelling to investigate these unusual cell morphologies from the perspective of balancing spatial amplification (i.e. positive feedback that localises components) with spatial tracking (i.e. negative feedback that allows sensing of gradient). First, they used generic models of cell polarity to explore different cell polarity behaviours that arose from changes in the model spatial dynamics. By exploring the positive and negative feedback loops in each stage of a two-stage model, they simulated a variety of cell morphologies including single bending projections, single straight projections, periodic multiple projections and simultaneous double projections. In the second half of the study, they used a two-stage mechanistic model of yeast cell polarity focusing on G-protein signalling to integrate the modelling results more closely with the authors' previously published experimental observations. In summary, the combination of modelling and experiments describes how yeast cells exhibit a diversity of cell morphologies arising from two-stage G-protein signalling dynamics modulated by positive and negative feedbacks.

Autoshortloop feedback regulation of pulsatile gonadotropin-releasing hormone (GnRH) secretion by its metabolite, GnRH-(1-5)

Given the central role of the decapeptide gonadotropin-releasing hormone (GnRH) in reproductive function, our long-term objective is to delineate the underlying mechanism regulating these reproductive processes. The outcome of GnRH secretion is in part dependent on the proteolytic metabolism of this decapeptide. In contrast to the belief that the metabolism of GnRH serves only as a degradative process that removes excess GnRH, we have shown that a metabolite of the decapeptide, GnRH-(1-5), can directly regulate GnRH gene expression and reproductive behavior. To further characterize the effect of GnRH-(1-5) on GnRH neuronal function, we determined whether GnRH-(1-5) can directly regulate GnRH secretion and pulsatility using an in vitro perifusion system. We compared the effect of GnRH-(1-5) on GnRH secretion in the immortalized GnRH neuron (GT1-7 cell line), whole rat hypothalamic explant, and enzymatically dispersed rat hypothalamic cells. Tissue preparations were perifused continuously for 9 h during which a 3-h challenge with GnRH-(1-5) was administered (4-6 h). The results show that treatment with GnRH-(1-5) increased (p < 0.05) the mean GnRH secretion and the amplitude of the pulses but not the pulse frequency. The present study supports the notion that GnRH-(1-5) is functionally capable of regulating the reproductive neuroendocrine system.

Ideomotor feedback control in a recurrent neural network

The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.

Effects of postprandial starvation on mRNA expression of endocrine-, amino acid and peptide transporter-, and metabolic enzyme-related genes in zebrafish (Danio rerio)

The goal of this study was to systematically evaluate the molecular activities of endocrine-, amino acid and peptide transporters-, and metabolic enzyme-related genes in 35-day-old mixed-sex zebrafish (Danio rerio) after feeding . Zebrafish with initial body weights ranging from 9 to 11 mg were fasted for 384 h in a controlled indoor environment. Fish were sampled at 0, 3, 6, 12, 24, 48, 96, 192, and 384 h after fed. Overall, the present study results show that the regulatory mechanism that insulin-like growth factor I negative feedback regulated growth hormone is conserved in zebrafish, as it is in mammals, but that regulation of growth hormone receptors is highly intricate. Leptin and cholecystokinin are time-dependent negative feedback signals, and neuropeptide Y may be an important positive neuropeptide for food intake in zebrafish. The amino acid/carnitine transporters B(0,+) (ATB(0,+)) and broad neutral (0) amino acid transporter 1(B(0)AT1) mRNA levels measured in our study suggest that protein may be utilized during 24-96 h of fasting in zebrafish. Glutamine synthetase mRNA levels were downregulated, and glutamate dehydrogenase, alanine aminotransferase, aspartate transaminase, and trypsin mRNA levels were upregulated after longtime fasting in this study. The mRNA expression levels of fatty acid synthetase decreased significantly (P < 0.05), whereas those of lipoprotein lipase rapidly increased after 96 h of fasting. Fasting activated the expression of glucose synthesis genes when fasting for short periods of time; when fasting is prolonged, the mRNA levels of glucose breakdown enzymes and pentose phosphate shunt genes decreased.

Compartmentalized nitric oxide signaling in the resistance vasculature

Nitric oxide (NO) was first described as a bioactive molecule through its ability to stimulate soluble guanylate cyclase, but the revelation that NO was the endothelium derived relaxation factor drove the field to its modern state. The wealth of research conducted over the past 30 years has provided us with a picture of how diverse NO signaling can be within the vascular wall, going beyond simple vasodilation to include such roles as signaling through protein S-nitrosation. This expanded view of NO's actions requires highly regulated and compartmentalized production. Importantly, resistance arteries house multiple proteins involved in the production and transduction of NO allowing for efficient movement of the molecule to regulate vascular tone and reactivity. In this review, we focus on the many mechanisms regulating NO production and signaling action in the vascular wall, with a focus on the control of endothelial nitric oxide synthase (eNOS), the enzyme responsible for synthesizing most of the NO within these confines. We also explore how cross talk between the endothelium and smooth muscle in the microcirculation can modulate NO signaling, illustrating that this one small molecule has the capability to produce a plethora of responses.

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.