Limited efficacy of adjuvant therapy with dexamethasone in preventing hearing loss due to experimental pneumococcal meningitis in the infant rat

Sensorineural hearing loss (SNHL) is the most common sequel of bacterial meningitis (BM) and is observed in up to 30% of survivors when the disease is caused by Streptococcus pneumoniae. BM is the single most important origin of acquired SNHL in childhood. Anti-inflammatory dexamethasone holds promises as potential adjuvant therapy to prevent SNHL associated with BM. However, in infant rats, pneumococcal meningitis (PM) increased auditory brainstem response (ABR) thresholds [mean difference = 54 decibels sound pressure level (dB SPL)], measured 3 wk after infection, irrespective to treatment with ceftriaxone plus dexamethasone or ceftriaxone plus saline (p < 0.005 compared with mock-infected controls). Moreover, dexamethasone did not attenuate short- and long-term histomorphologic correlates of SNHL. At 24 h after infection, blood-labyrinth barrier (BLB) permeability was significantly increased in infected animals of both treatment groups compared with controls. Three weeks after the infection, the averaged number of type I neurons per square millimeter of the Rosenthal's canal dropped from 0.3019 +/- 0.0252 in controls to 0.2227 +/- 0.0635 in infected animals receiving saline (p < 0.0005). Dexamethasone was not more effective than saline in preventing neuron loss (0.2462 +/- 0.0399; p > 0.05). These results suggest that more efficient adjuvant therapies are needed to prevent SNHL associated with pediatric PM.

Aerothermodynamic cycle analysis of a dual-spool, separate-exhaust turbofan engine with an interstage turbine burner

This study focuses on a specific engine, i.e., a dual-spool, separate-flow turbofan engine with an Interstage Turbine Burner (ITB). This conventional turbofan engine has been modified to include a secondary isobaric burner, i.e., ITB, in a transition duct between the high-pressure turbine and the low-pressure turbine. The preliminary design phase for this modified engine starts with the aerothermodynamics cycle analysis is consisting of parametric (i.e., on-design) and performance (i.e., off-design) cycle analyses. In parametric analysis, the modified engine performance parameters are evaluated and compared with baseline engine in terms of design limitation (maximum turbine inlet temperature), flight conditions (such as flight Mach condition, ambient temperature and pressure), and design choices (such as compressor pressure ratio, fan pressure ratio, fan bypass ratio etc.). A turbine cooling model is also included to account for the effect of cooling air on engine performance. The results from the on-design analysis confirmed the advantage of using ITB, i.e., higher specific thrust with small increases in thrust specific fuel consumption, less cooling air, and less NOx production, provided that the main burner exit temperature and ITB exit temperature are properly specified. It is also important to identify the critical ITB temperature, beyond which the ITB is turned off and has no advantage at all. With the encouraging results from parametric cycle analysis, a detailed performance cycle analysis of the identical engine is also conducted for steady-stateengine performance prediction. The results from off-design cycle analysis show that the ITB engine at full throttle setting has enhanced performance over baseline engine. Furthermore, ITB engine operating at partial throttle settings will exhibit higher thrust at lower specific fuel consumption and improved thermal efficiency over the baseline engine. A mission analysis is also presented to predict the fuel consumptions in certain mission phases. Excel macrocode, Visual Basic for Application, and Excel neuron cells are combined to facilitate Excel software to perform these cycle analyses. These user-friendly programs compute and plot the data sequentially without forcing users to open other types of post-processing programs.

A 45nm CMOS DESIGN OF NEUROMORPHIC SYSTEM

Neuromorphic computing has become an emerging field in wide range of applications. Its challenge lies in developing a brain-inspired architecture that can emulate human brain and can work for real time applications. In this report a flexible neural architecture is presented which consists of 128 X 128 SRAM crossbar memory and 128 spiking neurons. For Neuron, digital integrate and fire model is used. All components are designed in 45nm technology node. The core can be configured for certain Neuron parameters, Axon types and synapses states and are fully digitally implemented. Learning for this architecture is done offline. To train this circuit a well-known algorithm Restricted Boltzmann Machine (RBM) is used and linear classifiers are trained at the output of RBM. Finally, circuit was tested for handwritten digit recognition application. Future prospects for this architecture are also discussed.

Trial-to-trial size variability of motor-evoked potentials. A study using the triple stimulation technique

Motor-evoked potentials (MEPs) vary in size from one stimulus to the next. The objective of this study was to determine the cause and source of trial-to-trial MEP size variability. In two experiments involving 10 and 14 subjects, the variability of MEPs to cortical stimulation (cortical-MEPs) in abductor digiti minimi (ADM) and abductor hallucis (AH) was compared to those responses obtained using the triple stimulation technique (cortical-TST). The TST eliminates the effects of motor neuron (MN) response desynchronization and of repetitive MN discharges. Submaximal stimuli were used in both techniques. In six subjects, cortical-MEP variability was compared to that of brainstem-MEP and brainstem-TST. Variability was greater for MEPs than that for TST responses, by approximately one-third. The variability was the same for cortical- and brainstem-MEPs and was similar in ADM and AH. Variability concerned at least 10-15% of the MN pool innervating the target muscle. With the stimulation parameters used, repetitive MN discharges did not influence variability. For submaximal stimuli, approximately two-third of the observed MEP size variability is caused by the variable number of recruited alpha-MNs and approximately one-third by changing synchronization of MN discharges. The source of variability is most likely localized at the spinal segmental level.

Transfer Functions in Artificial Neural Networks - A Simulation-Based Tutorial

Artificial neural networks are based on computational units that resemble basic information processing properties of biological neurons in an abstract and simplified manner. Generally, these formal neurons model an input-output behaviour as it is also often used to characterize biological neurons. The neuron is treated as a black box; spatial extension and temporal dynamics present in biological neurons are most often neglected. Even though artificial neurons are simplified, they can show a variety of input-output relations, depending on the transfer functions they apply. This unit on transfer functions provides an overview of different transfer functions and offers a simulation that visualizes the input-output behaviour of an artificial neuron depending on the specific combination of transfer functions.

Modeling Calcium Concentration and Biochemical Reactions

Almost all regions of the brain receive one or more neuromodulatory inputs, and disrupting these inputs produces deficits in neuronal function. Neuromodulators act through intracellular second messenger pathways to influence the electrical properties of neurons, integration of synaptic inputs, spatio-temporal firing dynamics of neuronal networks, and, ultimately, systems behavior. Second messengers pathways consist of series of bimolecular reactions, enzymatic reactions, and diffusion. Calcium is the second messenger molecule with the most effectors, and thus is highly regulated by buffers, pumps and intracellular stores. Computational modeling provides an innovative, yet practical method to evaluate the spatial extent, time course and interaction among second messenger pathways, and the interaction of second messengers with neuron electrical properties. These processes occur both in compartments where the number of molecules are large enough to describe reactions deterministically (e.g. cell body), and in compartments where the number of molecules is small enough that reactions occur stochastically (e.g. spines). – In this tutorial, I explain how to develop models of second messenger pathways and calcium dynamics. The first part of the tutorial explains the equations used to model bimolecular reactions, enzyme reactions, calcium release channels, calcium pumps and diffusion. The second part explains some of the GENESIS, Kinetikit and Chemesis objects that implement the appropriate equations. In depth explanation of calcium and second messenger models is provided by reviewing code, both in XPP, Chemesis and Kinetikit, that implements simple models of calcium dynamics and second messenger cascades.

Simulating in vivo-like Synaptic Input Patterns in Multicompartmental Models

In the laboratory of Dr. Dieter Jaeger at Emory University, we use computer simulations to study how the biophysical properties of neurons—including their three-dimensional structure, passive membrane resistance and capacitance, and active membrane conductances generated by ion channels—affect the way that the neurons transfer synaptic inputs into the action potential streams that represent their output. Because our ultimate goal is to understand how neurons process and relay information in a living animal, we try to make our computer simulations as realistic as possible. As such, the computer models reflect the detailed morphology and all of the ion channels known to exist in the particular neuron types being simulated, and the model neurons are tested with synaptic input patterns that are intended to approximate the inputs that real neurons receive in vivo. The purpose of this workshop tutorial was to explain what we mean by ‘in vivo-like’ synaptic input patterns, and how we introduce these input patterns into our computer simulations using the freely available GENESIS software package (http://www.genesis-sim.org/GENESIS). The presentation was divided into four sections: first, an explanation of what we are talking about when we refer to in vivo-like synaptic input patterns

Virtual Electrophysiology with SPatch

SPatch is an open source virtual laboratory designed to perform simulated electrophysiological experiments without the technical difficulties inherent to laboratory work. It provides the core equipment necessary for recording neuronal activity and allows the user to install the equipment, design their own protocols, prepare solutions to bathe the preparation or to fill the electrodes, and gather data. Assistance is provided for most steps with predefined components that are appropriate to a range of standard procedures. Experiments that can be performed with SPatch at present concern the study of voltage-gated channels in isolated neurons. This allows understanding the ionic mechanisms of Na+ and Ca2+ action potentials, after spike hyperpolarization, pacemaker tonic or bursting activity of neurons, delayed or sustained or adaptive firing of neurons in response to a depolarization, spontaneous depolarization of the membrane following an hyperpolarization, etc. In an educational context, the main interest of SPatch is to allow students to focus on the concepts and thought processes of electrophysiological investigation without the high equipment costs and extensive training required to perform laboratory work. It can be used to acquaint students with the relevant procedures before starting work in a real lab, or to give students an understanding of single neuron behavior and the ways it can be studied without requiring practical work. We illustrate the function and use of SPatch, explore educational issues arising from the inevitable differences between simulated and real laboratory work, and outline possible improvements.

SNNAP: A Tool for Teaching Neuroscience

Simulation tools aid in learning neuroscience by providing the student with an interactive environment to carry out simulated experiments and test hypotheses. The field of neuroscience is well suited for the use of simulation tools since nerve cell signaling can be described by mathematical equations and solved by computer. Neural signaling entails the propagation of electrical current along nerve membrane and transmission to neighboring neurons through synaptic connections. Action potentials and synaptic transmission can be simulated and results displayed for visualization and analysis. The neurosimulator SNNAP (Simulator for Neural Networks and Action Potentials) is a simulation environment that provides users with editors for model building, simulator engine and visual display editor. This paper presents several modeling examples that illustrate some of the capabilities and features of SNNAP. First, the Hodgkin-Huxley (HH) model is presented and the threshold phenomenon is illustrated. Second, small neural networks are described with HH models using various synaptic connections available with SNNAP. Synaptic connections may be modulated through facilitation or depression with SNNAP. A study of vesicle pool dynamics is presented using an AMPA receptor model. Finally, a central pattern generator model of the Aplysia feeding circuit is illustrated as an example of a complex network that may be studied with SNNAP. Simulation code is provided for each case study described and tasks are suggested for further investigation.

Code-specific learning rules improve action selection by populations of spiking neurons

Population coding is widely regarded as a key mechanism for achieving reliable behavioral decisions. We previously introduced reinforcement learning for population-based decision making by spiking neurons. Here we generalize population reinforcement learning to spike-based plasticity rules that take account of the postsynaptic neural code. We consider spike/no-spike, spike count and spike latency codes. The multi-valued and continuous-valued features in the postsynaptic code allow for a generalization of binary decision making to multi-valued decision making and continuous-valued action selection. We show that code-specific learning rules speed up learning both for the discrete classification and the continuous regression tasks. The suggested learning rules also speed up with increasing population size as opposed to standard reinforcement learning rules. Continuous action selection is further shown to explain realistic learning speeds in the Morris water maze. Finally, we introduce the concept of action perturbation as opposed to the classical weight- or node-perturbation as an exploration mechanism underlying reinforcement learning. Exploration in the action space greatly increases the speed of learning as compared to exploration in the neuron or weight space.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

Vibrations, Posture, and the Stabilization of Gaze: An Experimental Study on Impedance Control

Vibrations, Posture, and the Stabilization of Gaze: An Experimental Study on Impedance Control R. KREDEL, A. GRIMM & E.-J. HOSSNER University of Bern, Switzerland Introduction Franklin and Wolpert (2011) identify impedance control, i.e., the competence to resist changes in position, velocity or acceleration caused by environmental disturbances, as one of five computational mechanisms which allow for skilled and fluent sen-sorimotor behavior. Accordingly, impedance control is of particular interest in situa-tions in which the motor task exhibits unpredictable components as it is the case in downhill biking or downhill skiing. In an experimental study, the question is asked whether impedance control, beyond its benefits for motor control, also helps to stabi-lize gaze what, in turn, may be essential for maintaining other control mechanisms (e.g., the internal modeling of future states) in an optimal range. Method In a 3x2x4 within-subject ANOVA design, 72 participants conducted three tests on visual acuity and contrast (Landolt / Grating and Vernier) in two different postures (standing vs. squat) on a platform vibrating at four different frequencies (ZEPTOR; 0 Hz, 4 Hz, 8 Hz, 12 Hz; no random noise; constant amplitude) in a counterbalanced or-der with 1-minute breaks in-between. In addition, perceived exertion (Borg) was rated by participants after each condition. Results For Landolt and Grating, significant main effects for posture and frequency are re-vealed, representing lower acuity/contrast thresholds for standing and for higher fre-quencies in general, as well as a significant interaction (p < .05), standing for in-creasing posture differences with increasing frequencies. Overall, performance could be maintained at the 0 Hz/standing level up to a frequency of 8 Hz, if bending of the knees was allowed. The fact that this result is not only due to exertion is proved by the Borg ratings showing significant main effects only, i.e., higher exertion scores for standing and for higher frequencies, but no significant interaction (p > .40). The same pattern, although not significant, is revealed for the Vernier test. Discussion Apparently, postures improving impedance control not only turn out to help to resist disturbances but also assist in stabilizing gaze in spite of these perturbations. Con-sequently, studying the interaction of these control mechanisms in complex unpre-dictable environments seems to be a fruitful field of research for the future. References Franklin, D. W., & Wolpert, D. M. (2011). Computational mechanisms of sensorimotor control. Neuron, 72, 425-442.

Antagonizing Nogo-receptor 1 promotes the number of cultured dopaminergic neurons and elongates their neurites

The myelin-associated protein Nogo-A and its receptor Nogo-receptor 1 (NgR1) are known as potent growth inhibitors of the adult central nervous system (CNS). Nogo-A is mostly expressed on the surface of oligodendrocytes, but is also found in neurons of the adult and developing CNS. This observation suggests that Nogo-A serves additional functions in the brain. Hence, in the present study, we investigated the effects of antagonizing NgR1 on cultured organotypic and dissociated dopaminergic neurons. For that purpose ventral mesencephalic cultures from E14 rat embryos were grown in absence or presence of the NgR1 antagonist NEP1-40 for 1 week. Treatment with NEP1-40 significantly increased cell densities of tyrosine hydroxylase-immunoreactive neurons. Moreover, organotypic ventral mesencephalic cultures displayed a significantly bigger volume after NEP1-40 treatment. Morphological analysis of tyrosine hydroxylase-positive neurons disclosed longer neurites and higher numbers of primary neurites in dissociated cultures incubated with NEP1-40, whereas soma size was not changed. In conclusion, our findings demonstrate that interfering with Nogo-A signaling by antagonizing NgR1 modulates dopaminergic neuron properties during development. These observations highlight novel aspects of the role of Nogo-A in the CNS and might have an impact in the context of Parkinson's disease.

Post-translational regulation of an Aplysia glutamate transporter during long-term facilitation.

Regulation of glutamate transporters accompanies plasticity of some glutamatergic synapses. The regulation of glutamate uptake at the Aplysia sensorimotor synapse during long-term facilitation (LTF) was investigated. Previously, increases in levels of ApGT1 (Aplysia glutamate transporter 1) in synaptic membranes were found to be related to long-term increases in glutamate uptake. In this study, we found that regulation of ApGT1 during LTF appears to occur post-translationally. Serotonin (5-HT) a transmitter that induces LTF did not increase synthesis of ApGT1. A pool of ApGT1 appears to exist in sensory neuron somata, which is transported to the terminals by axonal transport. Blocking the rough endoplasmic reticulum-Golgi-trans-Golgi network (TGN) pathway with Brefeldin A prevented the 5-HT-induced increase of ApGT1 in terminals. Also, 5-HT produced changes in post-translational modifications of ApGT1 as well as changes in the levels of an ApGT1-co-precipitating protein. These results suggest that regulation of trafficking of ApGT1 from the vesicular trafficking system (rough endoplasmic reticulum-Golgi-TGN) in the sensory neuron somata to the terminals by post-translational modifications and protein interactions appears to be the mechanism underlying the increase in ApGT1, and thus, glutamate uptake during memory formation.