Input-output Function Research Articles

Fourier method to function synthesis of an RCCC mechanism

The input—output function of an RCCC mechanism is investigated systematically by using the Fourier series method in this article. The relationship between the harmonic component of the input—output function and the dimensions of the RCCC mechanism is established. Based on this relationship, the basic dimensional type of the RCCC mechanism is determined. The problem of function synthesis of the RCCC mechanism is solved by setting up the numerical atlas database, which comprises 50 million basic dimensional types, and given the concrete steps of the function generator. Finally, an example is given to show the feasibility and the validity of this approach.

Taiwan's Yacht Industry: A Tale of Two Entrepreneurial Firms

This paper studies the evolution of Taiwan's yacht industry in general, and in particular we focus on two entrepreneurial firms, the Horizon Yacht Company and the Jade Yacht Company. Our purposes are two-fold. First, most research studies on Taiwan's economic success are based on the neoclassical economic model, which uses a proportional input-output production function, and emphasizes aggregate data to explain economic growth. We instead emphasize the role of entrepreneurship, which allows us to investigate closely how each individual firm discovers opportunities, exploits profits, and accumulates its capabilities to create perpetual wealth. Second, Taiwan is a very entrepreneurial society and its entrepreneurial spirit permeates into every corner with successful stories not just confined to some champion industries, such as integrated circuits (ICs), personal computers (PCs), etc. Taiwan's yacht industry, though it sailed through stormy periods in the late 1980s, has learned to grow to be a much more competitive player on the world stage. We show two cases of yacht corporations, Horizon and Jade, to shed light on how the firms use variant strategies to build their continuous competitive advantages, to meet challenges, and to galvanize their capabilities on their pathway to growth.

Hybrid Differential Evolution Technique for Economic Dispatch Problems

This paper is aimed at presenting techniques of hybrid differential evolution for solving various kinds of Economic Dispatch(ED) problems such as those including prohibited zones, emission dispatch, multiple fuels, and multiple areas. The results obtained for typical problems are compared with those obtained by other techniques such as Particle Swarm Optimization(PSO) and Classical Evolutionary Programming(CEP) techniques. The comparison of the results proves that hybrid differential evolution is quite favorable for solving ED problems with no restrictions on the shapes of the input-output functions of the generator.

Data-based mechanistic modelling of stochastic rainfall-flow processes by state dependent parameter estimation

Due to the inherent nonlinearity in the process of transformation of rainfall into river flow, a simple direct input-output transfer function (TF) model may not sufficiently capture the catchment's hydrological dynamics. This paper presents an application of state dependent parameter (SDP) models for nonlinear, stochastic dynamic system to identify the location and form of the nonlinearity in the rainfall-effective rainfall dynamics. The objective was to develop an effective rainfall input time series that was then used to improve the performance of an originally developed direct input-output TF model of daily rainfall-flow relationship. The CAPTAIN Toolbox in the MATLAB ® environment was used in the model identification in which the recursive filtering and smoothing procedures formulated within a stochastic state space setting were applied to the time series data in order to identify the location and form of nonlinearities within a generic TF model. The nonparametric estimation as well as the parametric optimisation of the resulting nonlinear models was done using the Curve Fitting Toolbox in MATLAB ®. The results showed an improved and more parsimonious TF model. The model improved from explaining only 13% of the data to 56% presenting an improvement of 43% in the model fit. The study demonstrates that simple stochastic but robust tools can be successfully applied to develop and improve applicable hydrological models.

Parameters to Maximize 2f2-f1 Distortion Product Otoacoustic Emission Levels

Past research has established parameters for the 2f1-f2 distortion product otoacoustic emissions (DPOAEs) that enhance response levels (e.g., L1 - L2 = 10 dB; f2/f1 = 1.22; L1, L2 = 65, 55 dB SPL). These same parameters do not optimize 2f2-f1 DPOAEs. Therefore, this study was conducted to evaluate more completely those parameters that produce the most robust 2f2-f1 output. Input-output functions of the 2f2-f1 component were obtained from 20 normal-hearing adults (with f2 = 2000 Hz and 4000 Hz). Frequency ratios, level differences, and overall sound levels were manipulated with parameter combinations chosen to expand on information established in previous studies. The DPOAE signal-to-noise ratio (SNR), response presence, and output levels were measured. In general, mean SNRs and 2f2-f1 levels were greater, and DPOAEs were present more often for 2000 than for 4000 Hz across all parameter combinations. No single parameter combination resulted in pronounced maxima for 2f2-f1, which is consistent with past studies but is in sharp contrast to results for the more familiar 2f1-f2. Overall, the results suggest that to maximize the 2f2-f1 level, lower test frequencies, low frequency ratio, essentially equal levels of primary tones, and moderate stimulus levels are best, and such parameters might be important for applying DPOAE measurement to assessment of normal and impaired auditory function.

Hierarchical adaptive experimental design for Gaussian process emulators

Large computer simulators have usually complex and nonlinear input output functions. This complicated input output relation can be analyzed by global sensitivity analysis; however, this usually requires massive Monte Carlo simulations. To effectively reduce the number of simulations, statistical techniques such as Gaussian process emulators can be adopted. The accuracy and reliability of these emulators strongly depend on the experimental design where suitable evaluation points are selected. In this paper a new sequential design strategy called hierarchical adaptive design is proposed to obtain an accurate emulator using the least possible number of simulations. The hierarchical design proposed in this paper is tested on various standard analytic functions and on a challenging reservoir forecasting application. Comparisons with standard one-stage designs such as maximin latin hypercube designs show that the hierarchical adaptive design produces a more accurate emulator with the same number of computer experiments. Moreover a stopping criterion is proposed that enables to perform the number of simulations necessary to obtain required approximation accuracy.

Using data envelopment analysis to quantify ILO objectives and identify policies conducive to decent work in a globalizing world

The International Labor Office, an arm of the UN based in Geneva, has as its goal the promotion of opportunities for women and men to obtain decent and productive work, in conditions of freedom, equity, security and human dignity. Since 1999, the ILO has conducted a series of studies of decent work. In 2001, the organization posed the global challenge of reducing the decent work deficit as measured by an employment gap, rights gap, a social protection gap, and a social dialogue gap. Using standard economic terms, “decent work” may be seen as an efficiency point along a generalized input–output function, dependent upon variables of both economic performance and economic and social policy. The decent work deficit of a given country (if any) is then obtained as the difference between an observed point and its projection on the efficiency frontier. Using data envelopment analysis (DEA), we fit a piecewise linear frontier to observations for 61 countries from all continents. Importantly, 27 of these countries lie on the decent work frontier; the remaining ones reveal conditions of decent work deficit. The possibilities of reducing such deficits by appropriate control of policy variables are discussed.

Cellular Mechanisms Underlying Stimulus-Dependent Gain Modulation in Primary Visual Cortex Neurons In Vivo

Gain modulation is a widespread neuronal phenomenon that modifies response amplitude without changing selectivity. Computational and in vitro studies have proposed cellular mechanisms of gain modulation based on the postsynaptic effects of background synaptic activation, but these mechanisms have not been studied in vivo. Here, we used intracellular recordings from cat primary visual cortex to measure neuronal gain while changing background synaptic activity with visual stimulation. We found that increases in the membrane fluctuations associated with increases in synaptic input do not obligatorily result in gain modulation in vivo. However, visual stimuli that evoked sustained changes in resting membrane potential, input resistance, and membrane fluctuations robustly modulated neuronal gain. The magnitude of gain modulation depended critically on the spatiotemporal properties of the visual stimulus. Gain modulation in vivo may thus be determined on a moment-to-moment basis by sensory context and the consequent dynamics of synaptic activation.

Rapid Neural Adaptation to Sound Level Statistics

Auditory neurons must represent accurately a wide range of sound levels using firing rates that vary over a far narrower range of levels. Recently, we demonstrated that this "dynamic range problem" is lessened by neural adaptation, whereby neurons adjust their input-output functions for sound level according to the prevailing distribution of levels. These adjustments in input-output functions increase the accuracy with which levels around those occurring most commonly are coded by the neural population. Here, we examine how quickly this adaptation occurs. We recorded from single neurons in the auditory midbrain during a stimulus that switched repeatedly between two distributions of sound levels differing in mean level. The high-resolution analysis afforded by this stimulus showed that a prominent component of the adaptation occurs rapidly, with an average time constant across neurons of 160 ms after an increase in mean level, much faster than our previous experiments were able to assess. This time course appears to be independent of both the timescale over which sound levels varied and that over which sound level distributions varied, but is related to neural characteristic frequency. We find that adaptation to an increase in mean level occurs more rapidly than to a decrease. Finally, we observe an additional, slow adaptation in some neurons, which occurs over a timescale of tens of seconds. Our findings provide constraints in the search for mechanisms underlying adaptation to sound level. They also have functional implications for the role of adaptation in the representation of natural sounds.

The effect of a precursor on growth of forward masking

This study examined the effect of an on-frequency precursor on growth-of-masking (GOM) functions measured using an off-frequency masker. The signal was a 6-ms, 4-kHz tone. A GOM function was measured using a 40-ms, 2.8-kHz tone (the off-frequency masker). GOM functions were then measured with an on-frequency, fixed level precursor presented before the off-frequency masker. The precursor was 50 or 60 dB SPL, and 160 ms in duration. For the 60-dB SPL precursor, a 40-ms duration was also used. Two-line functions were fit to the GOM data to estimate the basilar membrane input-output function. The precursors reduced the gain of the input-output function, and this decrease was graded with precursor level. Both precursor durations had the same effect on gain. Changes in masking following a precursor were larger than would be predicted by additivity of masking. The observed decrease in gain may be consistent with activation of the medial olivocochlear reflex by the precursor.

Representation of Nonlinear Random Transformations by Non-Gaussian Stochastic Neural Networks

The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.

Unmasking effects of olivocochlear efferent activation on responses of inferior colliculus neurons

Behavioural studies suggest a role for the medial olivocochlear (MOC) system in improving detection and discrimination of signals in noise. Physiological studies in the cochlea support this notion, showing unmasking, an improvement of the dynamic range of primary auditory afferents in response to tones in noise after MOC system activation. However, little is known about unmasking effects in higher centres. We investigated the effects of MOC stimulation on the responses of neurons in the guinea pig inferior colliculus to masked tones. In 50% of neurons background noise increased the basal rate and decreased maximum rate, an effect similar to that reported in primary afferents. In 35% of neurons maximum rate was increased by the background noise. Activation of the MOC system caused an increased slope and output dynamic range in the majority of input–output functions. Statistical analysis showed that this increased the discriminability of the tones within the noise. Hence, antimasking effects of olivocochlear activation that have been described in the cochlea can still be observed in the midbrain. However, 27% of neurons showed a variety of effects that have not been described in primary afferents, suggesting involvement of central circuitry in modulating effects of MOC stimulation on higher centres.

A visualization and segmentation toolbox for electron microscopy

Three-dimensional image analysis includes image processing, segmentation and visualization operations, which facilitate the interpretation of data. We have developed a toolbox for three-dimensional (3D) electron microscopy (EM) in Amira, which is a commercial software package, used by many laboratories. Our toolbox integrates a number of established procedures specifically tailored for 3D EM. These include input-output, filtering, segmentation, visualization and ray-tracing functions, which can be accessed directly from a user-friendly pop-up menu. They allow performing denoising and segmentation tasks directly in Amira, without the need of other programs, and ultimately allow the visualization of the results at photo-realistic quality with ray-tracing. They also allow a direct interaction with the data, such that, e.g., sub-tomograms can be directly extracted, or segmentation areas can be interactively selected. The implemented functions are fast, reliable and intuitive, yielding a comprehensive package for visualization in EM.

Dendritic Design Implements Algorithm for Synaptic Extraction of Sensory Information

While sensory information is encoded by firing patterns of individual sensory neurons, it is also represented by spatiotemporal patterns of activity in populations of the neurons. Postsynaptic interneurons decode the population response and extract specific sensory information. This extraction of information represented by presynaptic activities is a process critical to defining the input-output function of postsynaptic neuron. To understand the "algorithm" for the extraction, we examined directional sensitivities of presynaptic and postsynaptic Ca(2+) responses in dendrites of two types of wind-sensitive interneurons (INs) with different dendritic geometries in the cricket cercal sensory system. In IN 10-3, whose dendrites arborize with various electrotonic distances to the spike-initiating zone (SIZ), the directional sensitivity of dendritic Ca(2+) responses corresponded to those indicated by Ca(2+) signals in presynaptic afferents arborizing on that dendrite. The directional tuning properties of individual dendrites varied from each other, and the directional sensitivity of the nearest dendrite to the SIZ dominates the tuning properties of the spiking response. In IN 10-2 with dendrites isometric to the SIZ, directional tuning properties of different dendrites were similar to each other, and each response property could be explained by the directional profile of the spatial overlap between that dendrite and Ca(2+)-elevated presynaptic terminals. For IN 10-2, the directional sensitivities extracted by the different dendritic-branches would contribute equally to the overall tuning. It is possible that the differences in the distribution of synaptic weights because of the dendritic geometry are related to the algorithm for extraction of sensory information in the postsynaptic interneurons.

Age-related hearing loss in the Fischer 344/NHsd rat substrain

Studies of the F344 rat have shown a variety of age-related auditory anatomy and physiology changes. The current study was undertaken to clarify the ARHL in the F344 rat, by examining the auditory pathway of the F344/NHsd substrain that is distributed by Harlan Laboratories for research in the United States. The F344/NHsd rat begins to lose its hearing at about 12 months, and by 24 months, there are 50–60 dB auditory brainstem response threshold shifts at 20 and 40 kHz and 20 dB losses at 5–10 kHz. Distortion product otoacoustic emissions (DPOAE) amplitudes at 1.8–12 kHz stimuli were depressed in the older (18–24 months) rats. Amplitude input–output functions of the compound action potential (CAP) were also depressed across frequency. The endocochlear potential (EP) was 90–100 mV in the 3 month old rats. All but one of the 24 month old rats’ EPs were in the +75–85 mV range. Tympanometry revealed no differences in middle ear function between the young and older rats. Collectively, these findings suggest damage to the outer hair cells, but anatomical examination of the outer hair cells revealed a relative lack of cell loss compared to the magnitude of the hearing and DPOAE loss.

Dopamine Increases the Gain of the Input-Output Response of Rat Prefrontal Pyramidal Neurons

Dopaminergic modulation of prefrontal cortical activity is known to affect cognitive functions like working memory. Little consensus on the role of dopamine modulation has been achieved, however, in part because quantities directly relating to the neuronal substrate of working memory are difficult to measure. Here we show that dopamine increases the gain of the frequency-current relationship of layer 5 pyramidal neurons in vitro in response to noisy input currents. The gain increase could be attributed to a reduction of the slow afterhyperpolarization by dopamine. Dopamine also increases neuronal excitability by shifting the input-output functions to lower inputs. The modulation of these response properties is mainly mediated by D1 receptors. Integrate-and-fire neurons were fitted to the experimentally recorded input-output functions and recurrently connected in a model network. The gain increase induced by dopamine application facilitated and stabilized persistent activity in this network. The results support the hypothesis that catecholamines increase the neuronal gain and suggest that dopamine improves working memory via gain modulation.

Statistical approach in search for optimal signal in simple olfactory neuronal models

Several models (concentration detectors and a flux detector) for coding of odor intensity in olfactory sensory neurons are investigated. Behavior of the system is described by different stochastic processes of binding the odorant molecules to the receptors and their activation. Characteristics how well the odorant concentration can be estimated from the knowledge of response, the number of activated neurons, are studied. The approach is based on the Fisher information and analogous measures. These measures of optimality are computed and applied to locate the odorant concentration which is most suitable for coding. The results are compared with the classical deterministic approach which judges the optimal odorant concentration via steepness of the input–output function.

Action control of autonomous agents in continuous valued space using RFCN

AbstractResearchers on action control of autonomous agents and multiple agents have attracted increasing attention in recent years. The general methods using action control of agents are neural network, genetic programming, and reinforcement learning. In this study, we use neural network for action control of autonomous agents. Our method determines the structure and parameter of neural network in evolution. We proposed Flexibly Connected Neural Network (FCN) previously as a method of constructing arbitrary neural networks with optimized structures and parameters to solve unknown problems. FCN was applied to action control of an autonomous agent and showed experimentally that it is effective for perceptual aliasing problems. All of the experiments of FCN, however, are only in grid space. In this paper, we propose a new method based on FCN which can decide correction action in real and continuous valued space. The proposed method, called Real‐valued FCN (RFCN), optimizes input–output functions of each unit, parameters of the input–output functions and speed of each unit. In order to examine its effectiveness, we applied the proposed method to action control of an autonomous agent to solve continuous‐valued maze problems. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(2): 31–39, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10032

Effects of centrifugal pathways on responses of cochlear nucleus neurons to signals in noise

The medial olivocochlear (MOC) system, which originates in the brainstem and projects to the outer hair cells in the cochlea, is thought to be involved in improving signal detection in noisy backgrounds. This proposition arises from the observation that the input-output functions of auditory primary afferent fibres to pure tones recorded in a continuous background noise are unmasked by MOC activation, improving the dynamic range, and is supported by both animal and human behavioural experiments. However, it is not known how the unmasking effects observed in the cochlea are translated into higher auditory brain centres, such as the cochlear nucleus, where intrinsic circuitry can potentially modulate any effect. In this study we have investigated the effects of continuous background noise without and with MOC system activation, on responses of different neuron types in the ventral cochlear nucleus of the guinea pig. Results show that the unmasking effects of MOC system activation on tone responses in continuous background noise are present in the cochlear nucleus. These unmasking effects manifest themselves as decompression of input-output functions as well as an improved slope, which results in an improvement in intensity discrimination of the tones. The data show, however, that the strength of the unmasking effects of MOC system activation varies between the different neuronal types. Unmasking was not detected in onset chopper neurons despite its demonstrable presence in other neuronal types in the same animals. These observations may reflect the level of involvement of different neuronal types in intensity discrimination.

The relationship between precursor level and the temporal effect.

Previous studies have suggested that temporal effects in masking may be consistent with a decrease in cochlear gain. One paradigm used to show this is to measure the level of a long-duration masker required to just mask a short-duration tone that occurs near masker onset. The temporal effect is revealed when the signal is detected at a lower signal-to-noise ratio following preceding stimulation (either an extension of the masker or a separate precursor). The present study examined whether this effect depends on precursor level. The signal was a 10-ms, 4-kHz tone. The masker was 200 ms. A fixed-level precursor had the same frequency characteristics as the masker, and was 205 ms. The masker and precursor had either no notch or a wide notch about the signal frequency. For a given precursor level, the growth of masker level with signal level was determined. These data were used to estimate input-output functions. The results are consistent with a graded decrease in gain at the signal frequency when there is no notch in the masker and precursor, and a graded decrease in suppression when there is a large notch. These results could be consistent with the action of the medial olivocochlear reflex.