Effect of a feeding stimulant on feeding adaptation of gibel carp Carassius auratus gibelio (Bloch), fed diets with replacement of fish meal by meat and bone meal

The objectives of the study were to investigate the effect of a feeding stimulant on feeding adaptation of gibel carp (Carassius auratus gibelio Bloch) fed diets with replacement of fish meal by meat and bone meal (MBM), and whether or not the juvenile gibel carp could adapt to higher MBM level in the diet. Juvenile and adult gibel carp were tested. Two and one replacement levels were used for juvenile and adult fish respectively. Each group of diets was set as two types with or without a unique rare earth oxide: Y2O3, Yb2O3, La2O3, Sm2O3, Nd2O3 or Gd2O3 (only the first four rare earth oxides were used in adult diets) for four adaptation periods of 3, 7, 14 and 28 days respectively. After mixing, an equal mixture of all six diets for juvenile or four diets for adult was offered in excess for 2 days. During the last 2 days of each experiment, no feed was offered and faeces from each tank were collected. Feeding preference was expressed as relative feed intake of each diet, which was estimated based on the relative concentration of each marker in the faeces. Given some adaptation period, such as 3-28 days, the effects of MBM and squid extract inclusion on the preference to each diet were reduced. After 28 days adaptation, the preferences between groups were not significantly different.

Independent adaptation to riverine habitats allowed survival of ancient cetacean lineages

The four species of "river dolphins" are associated with six separate great river systems on three subcontinents and have been grouped for more than a century into a single taxon based on their similar appearance. However, several morphologists recently questioned the monophyly of that group. By using phylogenetic analyses of nucleotide sequences from three mitochondrial and two nuclear genes, we demonstrate with statistical significance that extant river dolphins are not monophyletic and suggest that they are relict species whose adaptation to riverine habitats incidentally insured their survival against major environmental changes in the marine ecosystem or the emergence of Delphinidae.

A VSLMS Style Tap-length Learning Algorithm for Structure Adaptation

Compared with the ordinary adaptive filter, the variable-length adaptive filter is more efficient (including smaller., lower power consumption and higher computational complexity output SNR) because of its tap-length learning algorithm, which is able to dynamically adapt its tap-length to the optimal tap-length that best balances the complexity and the performance of the adaptive filter. Among existing tap-length algorithms, the LMS-style Variable Tap-Length Algorithm (also called Fractional Tap-Length Algorithm or FT Algorithm) proposed by Y.Gong has the best performance because it has the fastest convergence rates and best stability. However, in some cases its performance deteriorates dramatically. To solve this problem, we first analyze the FT algorithm and point out some of its defects. Second, we propose a new FT algorithm called 'VSLMS' (Variable Step-size LMS) Style Tap-Length Learning Algorithm, which not only uses the concept of FT but also introduces a new concept of adaptive convergence slope. With this improvement the new FT algorithm has even faster convergence rates and better stability. Finally, we offer computer simulations to verify this improvement.

Neuro-Stimulus Chip with Photodiodes Array for Sub-retinal Implants

A prototype neuro-stimulus chip for sub-retinal implants in blind patients affected by Age-related Macular Degeneration (AMD) or Retinitis Pigmentosa (RP) is presented in this paper. This retinal prosthetic chip was designed to replace the degenerated photoreceptor cells, and in order to stimulate directly the remaining healthy layers of retinal neurons. The current stimulus circuits are monolithic integrated with photodiodes (PD) array, which can convert the illumination on the eyes into bi-phasic electrical pulses. In addition, a novel charge cancellation circuit is used to discharge the electrodes for medical safty. The prototype chip is designed and fabricated in HJTC 0.18 mu m N-well CMOS 1P6M Mix-signal process, with a +/- 2.5 V dual voltage power supply.

Kinetic spectra of light-adaptation dark-adaptation and M-intermediate of BR-D96N

BR-D96N is a kind of genetically site-specific mutants of bacteriorhodopsin (BR) with obvious photochromic effect. Compared to the wild type BR, the lifetime of M state of BR-D96N is prolonged to several minutes so that the photochromic kinetics and the intermediates formation can be studied by the conventional spectra analysis. In the experiment, the absorption spectra of the sample at different time after light illumination are measured with spectrophotometer. By fitting and analyzing the variation of the spectra, we suppose that there are three main states in the, photochromic process, i.e., B state (light-adapted state), M state and D state (dark-adapted state). The absorption spectra of the B state, M state and D state are extracted from the experimental data based on this three-state model and the spectra at various time are fitted with the least-square method. So, the variations of population percentages of the M state, B state and D state are obtained and the M state and B state lifetimes are estimated. In another way, from the measurement of the absorption dynamics at 407 and 568 nm, the M state and B state lifetimes are also obtained by two exponential data fitting, which give coincident results with those of the spectra analysis. (C) 2003 Elsevier Science B.V. All rights reserved.

A stochastic approximation algorithm with step size adaptation

Plakhov, A.Y.; Cruz, P., (2004) 'A stochastic approximation algorithm with step size adaptation', Journal of Mathematical Science 120(1) pp.964-973 RAE2008

Exploiting the Transients of Adaptation for RoQ Attacks on Internet Resources

In this paper, we expose an unorthodox adversarial attack that exploits the transients of a system's adaptive behavior, as opposed to its limited steady-state capacity. We show that a well orchestrated attack could introduce significant inefficiencies that could potentially deprive a network element from much of its capacity, or significantly reduce its service quality, while evading detection by consuming an unsuspicious, small fraction of that element's hijacked capacity. This type of attack stands in sharp contrast to traditional brute-force, sustained high-rate DoS attacks, as well as recently proposed attacks that exploit specific protocol settings such as TCP timeouts. We exemplify what we term as Reduction of Quality (RoQ) attacks by exposing the vulnerabilities of common adaptation mechanisms. We develop control-theoretic models and associated metrics to quantify these vulnerabilities. We present numerical and simulation results, which we validate with observations from real Internet experiments. Our findings motivate the need for the development of adaptation mechanisms that are resilient to these new forms of attacks.

Friendly Virtual Machine: Leveraging a Feedback-Control Model for Application Adaptation

With the increased use of "Virtual Machines" (VMs) as vehicles that isolate applications running on the same host, it is necessary to devise techniques that enable multiple VMs to share underlying resources both fairly and efficiently. To that end, one common approach is to deploy complex resource management techniques in the hosting infrastructure. Alternately, in this paper, we advocate the use of self-adaptation in the VMs themselves based on feedback about resource usage and availability. Consequently, we define a "Friendly" VM (FVM) to be a virtual machine that adjusts its demand for system resources, so that they are both efficiently and fairly allocated to competing FVMs. Such properties are ensured using one of many provably convergent control rules, such as AIMD. By adopting this distributed application-based approach to resource management, it is not necessary to make assumptions about the underlying resources nor about the requirements of FVMs competing for these resources. To demonstrate the elegance and simplicity of our approach, we present a prototype implementation of our FVM framework in User-Mode Linux (UML)-an implementation that consists of less than 500 lines of code changes to UML. We present an analytic, control-theoretic model of FVM adaptation, which establishes convergence and fairness properties. These properties are also backed up with experimental results using our prototype FVM implementation.

A Model of Cerebellar Adaptation of Grip Forces During Lifting

We investigated adaptive neural control of precision grip forces during object lifting. A model is presented that adjusts reactive and anticipatory grip forces to a level just above that needed to stabilize lifted objects in the hand. The model obeys priciples of cerebellar structure and function by using slip sensations as error signals to adapt phasic motor commands to tonic force generators associated with output synergies controlling grip aperture. The learned phasic commands are weight and texture-dependent. Simulations of the new curcuit model reproduce key aspects of experimental observations of force application. Over learning trials, the onset of grip force buildup comes to lead the load force buildup, and the rate-of-rise of grip force, but not load force, scales inversely with the friction of the gripped object.

Adaptation of Dopamine Neurons' Mismatch Sensitivity Is not Compatible With Reinforcement Learning Theory

Recent electrophysical data inspired the claim that dopaminergic neurons adapt their mismatch sensitivities to reflect variances of expected rewards. This contradicts reward prediction error theory and most basal ganglia models. Application of learning principles points to a testable alternative interpretation-of the same data-that is compatible with existing theory.

Simulations of X and Y Retinal Ganglion Cell Behavior with a Nonlinear Push-pull Model of Spatiotemporal Retinal Processing

This article describes a nonlinear model of neural processing in the vertebrate retina, comprising model photoreceptors, model push-pull bipolar cells, and model ganglion cells. Previous analyses and simulations have shown that with a choice of parameters that mimics beta cells, the model exhibits X-like linear spatial summation (null response to contrast-reversed gratings) in spite of photoreceptor nonlinearities; on the other hand, a choice of parameters that mimics alpha cells leads to Y-like frequency doubling. This article extends the previous work by showing that the model can replicate qualitatively many of the original findings on X and Y cells with a fixed choice of parameters. The results generally support the hypothesis that X and Y cells can be seen as functional variants of a single neural circuit. The model also suggests that both depolarizing and hyperpolarizing bipolar cells converge onto both ON and OFF ganglion cell types. The push-pull connectivity enables ganglion cells to remain sensitive to deviations about the mean output level of nonlinear photoreceptors. These and other properties of the push-pull model are discussed in the general context of retinal processing of spatiotemporal luminance patterns.

A Neural Network Model for the Spatial and Temporal Response of Retinal Ganglion Cells

This article introduces a quantitative model of early visual system function. The model is formulated to unify analyses of spatial and temporal information processing by the nervous system. Functional constraints of the model suggest mechanisms analogous to photoreceptors, bipolar cells, and retinal ganglion cells, which can be formally represented with first order differential equations. Preliminary numerical simulations and analytical results show that the same formal mechanisms can explain the behavior of both X (linear) and Y (nonlinear) retinal ganglion cell classes by simple changes in the relative width of the receptive field (RF) center and surround mechanisms. Specifically, an increase in the width of the RF center results in a change from X-like to Y-like response, in agreement with anatomical data on the relationship between α- and

A Unified Model of Spatiotemporal Processing in the Retina

A computational model of visual processing in the vertebrate retina provides a unified explanation of a range of data previously treated by disparate models. Three results are reported here: the model proposes a functional explanation for the primary feed-forward retinal circuit found in vertebrate retinae, it shows how this retinal circuit combines nonlinear adaptation with the desirable properties of linear processing, and it accounts for the origin of parallel transient (nonlinear) and sustained (linear) visual processing streams as simple variants of the same retinal circuit. The retina, owing to its accessibility and to its fundamental role in the initial transduction of light into neural signals, is among the most extensively studied neural structures in the nervous system. Since the pioneering anatomical work by Ramón y Cajal at the turn of the last century[1], technological advances have abetted detailed descriptions of the physiological, pharmacological, and functional properties of many types of retinal cells. However, the relationship between structure and function in the retina is still poorly understood. This article outlines a computational model developed to address fundamental constraints of biological visual systems. Neurons that process nonnegative input signals-such as retinal illuminance-are subject to an inescapable tradeoff between accurate processing in the spatial and temporal domains. Accurate processing in both domains can be achieved with a model that combines nonlinear mechanisms for temporal and spatial adaptation within three layers of feed-forward processing. The resulting architecture is structurally similar to the feed-forward retinal circuit connecting photoreceptors to retinal ganglion cells through bipolar cells. This similarity suggests that the three-layer structure observed in all vertebrate retinae[2] is a required minimal anatomy for accurate spatiotemporal visual processing. This hypothesis is supported through computer simulations showing that the model's output layer accounts for many properties of retinal ganglion cells[3],[4],[5],[6]. Moreover, the model shows how the retina can extend its dynamic range through nonlinear adaptation while exhibiting seemingly linear behavior in response to a variety of spatiotemporal input stimuli. This property is the basis for the prediction that the same retinal circuit can account for both sustained (X) and transient (Y) cat ganglion cells[7] by simple morphological changes. The ability to generate distinct functional behaviors by simple changes in cell morphology suggests that different functional pathways originating in the retina may have evolved from a unified anatomy designed to cope with the constraints of low-level biological vision.