589 research outputs found
Maximising information transfer through nonlinear noisy devices
No full text© 2003 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.Consider an array of parallel comparators (threshold devices) receiving the same input signal, but subject to independent noise, where the output from each device is summed to give an overall output. Such an array is a good model of a number of nonlinear systems including flash analogue to digital converters, sonar arrays and parallel neurons. Recently, this system was analysed by Stocks in terms of information theory, who showed that under certain conditions the transmitted information through the array is maximised for non-zero noise. This phenomenon was termed Suprathreshold Stochastic Resonance (SSR). In this paper we give further results related to the maximisation of the transmitted information in this system.Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce, and Derek Abbot
Stochastic resonance in electrical circuits—II: Nonconventional stochastic resonance.
No full textStochastic resonance (SR), in which a periodic signal in a nonlinear system can be amplified by added noise, is discussed. The application of circuit modeling techniques to the conventional form of SR, which occurs in static bistable potentials, was considered in a companion paper. Here, the investigation of nonconventional forms of SR in part using similar electronic techniques is described. In the small-signal limit, the results are well described in terms of linear response theory. Some other phenomena of topical interest, closely related to SR, are also treate
Stochastic resonance in electrical circuits—I: Conventional stochastic resonance.
No full textStochastic resonance (SR), a phenomenon in which a periodic signal in a nonlinear system can be amplified by added noise, is introduced and discussed. Techniques for investigating SR using electronic circuits are described in practical terms. The physical nature of SR, and the explanation of weak-noise SR as a linear response phenomenon, are considered. Conventional SR, for systems characterized by static bistable potentials, is described together with examples of the data obtainable from the circuit models used to test the theory
Effectiveness of seasonal influenza vaccine in Australia, 2015: an epidemiological, antigenic and phylogenetic assessment
No full textAbstract not availableJames E. Fielding, Avram Levy, Monique B. Chilver, Yi-Mo Deng, Annette K. Regan, Kristina A. Grant, Nigel P. Stocks, Sheena G. Sulliva
Safety of Ceasing Aspirin Used Without a Clinical Indication After Age 70 Years: A Subgroup Analysis of the ASPREE Randomized Trial
No full textLettersAbstract not availableMark R. Nelson, Galina Polekhina, Robyn L. Woods, Christopher M. Reid, Andrew M. Tonkin, Rory Wolfe, Anne M. Murray, Brenda Kirpach, Michael E. Ernst, Jessica E. Lockery, Raj C. Shah, Nigel Stocks, Suzanne G. Orchard, Zhen Zho
Optimal quantization and suprathreshold stochastic resonance
No full text©2005 COPYRIGHT SPIE--The International Society for Optical EngineeringIt is shown that Suprathreshold Stochastic Resonance (SSR) iseffectively a way of using noise to perform quantization or lossysignal compression with a population of identical threshold-baseddevices. Quantization of an analog signal is a fundamentalrequirement for its efficient storage or compression in a digitalsystem. This process will always result in a loss of quality,known as distortion, in a reproduction of the original signal. Thedistortion can be decreased by increasing the number of statesavailable for encoding the signal (measured by the rate, or mutualinformation). Hence, designing a quantizer requires a tradeoffbetween distortion and rate. Quantization theory has recently beenapplied to the analysis of neural coding and here we examine thepossibility that SSR is a possible mechanism used by populationsof sensory neurons to quantize signals. In particular, we analyzethe rate-distortion performance of SSR for a range of input SNR'sand show that both the optimal distortion and optimal rate occursfor an input SNR of about 0 dB, which is a biologically plausiblesituation. Furthermore, we relax the constraint that allthresholds are identical, and find the optimal threshold valuesfor a range of input SNRs. We find that for sufficiently smallinput SNRs, the optimal quantizer is one in which all thresholdsare identical, that is, the SSR situation is optimal in this case.Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce, and Derek Abbot
Analog to digital conversion using suprathreshold stochastic resonance
No full text© 2005 COPYRIGHT SPIE--The International Society for Optical Engineering Copyright 2004 Society of Photo-Optical Instrumentation Engineers. This paper was published in Smart Structures, Devices, and Systems II, edited by Said F. Al-Sarawi, Proceedings of SPIE Vol. 5649 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.We present an analysis of the use of suprathreshold stochastic resonance for analog to digital conversion. Suprathreshold stochastic resonance is a phenomenon where the presence of internal or input noise provides the optimal response from a system of identical parallel threshold devices such as comparators or neurons. Under the conditions where this occurs, such a system is effectively a non-deterministic analog to digital converter. In this paper we compare the suprathreshold stochastic resonance effect to conventional analog to digital conversion by analysing the rate-distortion trade-off of each.Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce, and Derek Abbot
How to use noise to reduce complexity in quantization
No full text© 2006 COPYRIGHT SPIE--The International Society for Optical EngineeringConsider a quantization scheme which has the aim of quantizing a signal into N+1 discrete output states. The specification of such a scheme has two parts. Firstly, in the encoding stage, the specification of N unique threshold values is required. Secondly, the decoding stage requires specification of N+1 unique reproduction values. Thus, in general, 2N+1 unique values are required for a complete specification. We show in this paper how noise can be used to reduce the number of unique values required in the encoding stage. This is achieved by allowing the noise to effectively make all thresholds independent random variables, the end result being a stochastic quantization. This idea originates from a form of stochastic resonance known as suprathreshold stochastic resonance. Stochastic resonance occurs when noise in a system is essential for that system to provide its optimal output and can only occur in nonlinear systems--one prime example being neurons. The use of noise requires a tradeoff in performance, however, we show that even very low signal-to-noise ratios can provide a reasonable average performance for a substantial reduction in complexity, and that high signal-to-noise ratios can also provide a reduction in complexity for only a negligible degradation in performance.Mark D. McDonnell, Nigel G. Stocks, Charles E.M. Pearce, and Derek Abbot
Stochastic Pooling Networks: a biologically inspired model for robust signal detection and compression
No full textStochastic Pooling Networks (SPN) were recently introduced as a general conceptual framework for modeling surprising nonlinear interactions between redundancy and two forms of dasianoisepsila: lossy compression and randomness. The SPN approach arose from studies of biological signal transduction by populations of sensory neurons, but is also suitable for modeling several modern communications and computing paradigms. The common feature required is that lossy compression and the noise-averaging affects of redundancy occur simultaneously. To illustrate the potential for bio-inspired engineering that mimics neural SPNs, here we illustrate some interesting features of a very simple SPN, where individual network nodes are extremely compressive, and provide only single-bit measurements of analog signals. Information theory is used to quantify the gain obtained from N such measurements. We show that network performance is limited by quantization noise for large input SNRs, but is limited only by the size of the network for small input SNRs. The latter case is shown to approach the performance of a network where there is no lossy compression, indicating that extreme local compression is close to optimal. Finally, interpretation of the mutual information results in terms of both rate-distortion theory, and probability of error are given.Mark D. McDonnell, Pierre-Olivier Amblard, Nigel G. Stock
Optimal quantization for energy-efficient information transfer in a population of neuron-like devices
No full text© 2004 COPYRIGHT SPIE--The International Society for Optical EngineeringSuprathreshold Stochastic Resonance (SSR) is a recently discoveredform of stochastic resonance that occurs in populations of neuron-like devices. A key feature of SSR is that all devices in the population possess identical threshold nonlinearities. It haspreviously been shown that information transmission through such asystem is optimized by nonzero internal noise. It is also clearthat it is desirable for the brain to transfer information in anenergy efficient manner. In this paper we discuss the energy efficient maximization of information transmission for the case ofvariable thresholds and constraints imposed on the energy available to the system, as well as minimization of energy for the case of a fixed information rate. We aim to demonstrate that under certain conditions, the SSR configuration of all devices having identical thresholds is optimal. The novel feature of this work is that optimization is performed by finding the optimal threshold settings for the population of devices, which is equivalent to solving a noisy optimal quantization problem.Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce, and Derek Abbot
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