The mechanism for stochastic resonance enhancement of mammalian auditory information processing

Background  

In a mammalian auditory system, when intrinsic noise is added to a subthreshold signal, not only can the resulting noisy signal be detected, but also the information carried by the signal can be completely recovered. Such a phenomenon is called stochastic resonance (SR). Current analysis of SR commonly employs the energies of the subthreshold signal and intrinsic noise. However, it is difficult to explain SR when the energy addition of the signal and noise is not enough to lift the subthreshold signal over the threshold. Therefore, information modulation has been hypothesized to play a role in some forms of SR in sensory systems. Information modulation, however, seems an unlikely mechanism for mammalian audition, since it requires significant a priori knowledge of the characteristics of the signal.     

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction. Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.     

The stochastic properties of the basic neuron populations as information processing system

This paper deals with the stochastic properties of the simplest neuron population consists of two neurons. Two modes of neural coupling are discussed. One is the forward inhibition mode [FI] and the other is the backward inhibition mode [BI]. In the forward inhibition mode, the two neurons are assumed to be independent of each other. In the backward inhibition mode, the two neurons interact, but the inputs to the neurons are assumed to be independent of each other. In the analysis, we first obtain the probability density function [p.d.f.] of the interspike intervals of the output impulse trains in FI and BI. Then we define the mean rate transfer function from the mean rate of these p.d.f.'s. Finally, by comparing our analytical results with the physiological experimental data, it is clear that the difference in the stochastic properties can be accounted for by the difference in the coupling mode (i.e. FI or BI).     

Fisher information for a multivariate extreme value distribution

Explicit algebraic formulae for the Fisher information matrixof the multivariate extreme value distribution with generalisedextreme value margins and logistic dependence structure aregiven.     

A stochastic model and a functional central limit theorem for information processing in large systems of neurons

The paper deals with information transmission in large systems of neurons. We model the membrane potential in a single neuron belonging to a cell tissue by a non time-homogeneous Cox-Ingersoll-Ross type diffusion; in terms of its time-varying expectation, this stochastic process can convey deterministic signals. We model the spike train emitted by this neuron as a Poisson point process compensated by the occupation time of the membrane potential process beyond the excitation threshold. In a large system of neurons 1≤i≤N processing independently the same deterministic signal, we prove a functional central limit theorem for the pooled spike train collected from the N neurons. This pooled spike train allows to recover the deterministic signal, up to some shape transformation which is explicit.     

Energy as a constraint on the coding and processing of sensory information

Neurons use significant amounts of energy to generate signals. Recent studies of retina and brain connect this energy usage to the ability to transmit information. The identification of energy-efficient neural circuits and codes suggests new ways of understanding the function, design and evolution of nervous systems.     

Fisher information matrix of the Dirichlet-multinomial distribution

In this paper we derive explicit expressions for the elements of the exact Fisher information matrix of the Dirichlet-multinomial distribution. We show that exact calculation is based on the beta-binomial probability function rather than that of the Dirichlet-multinomial and this makes the exact calculation quite easy. The exact results are expected to be useful for the calculation of standard errors of the maximum likelihood estimates of the beta-binomial parameters and those of the Dirichlet-multinomial parameters for data that arise in practice in toxicology and other similar fields. Standard errors of the maximum likelihood estimates of the beta-binomial parameters and those of the Dirichlet-multinomial parameters, based on the exact and the asymptotic Fisher information matrix based on the Dirichlet distribution, are obtained for a set of data from Haseman and Soares (1976), a dataset from Mosimann (1962) and a more recent dataset from Chen, Kodell, Howe and Gaylor (1991). There is substantial difference between the standard errors of the estimates based on the exact Fisher information matrix and those based on the asymptotic Fisher information matrix.     

Combinatorics of locally optimal RNA secondary structures

It is a classical result of Stein and Waterman that the asymptotic number of RNA secondary structures is $1.104366 \cdot n^{-3/2} \cdot 2.618034^n$ . Motivated by the kinetics of RNA secondary structure formation, we are interested in determining the asymptotic number of secondary structures that are locally optimal, with respect to a particular energy model. In the Nussinov energy model, where each base pair contributes $-1$ towards the energy of the structure, locally optimal structures are exactly the saturated structures, for which we have previously shown that asymptotically, there are $1.07427\cdot n^{-3/2} \cdot 2.35467^n$ many saturated structures for a sequence of length $n$ . In this paper, we consider the base stacking energy model, a mild variant of the Nussinov model, where each stacked base pair contributes $-1$ toward the energy of the structure. Locally optimal structures with respect to the base stacking energy model are exactly those secondary structures, whose stems cannot be extended. Such structures were first considered by Evers and Giegerich, who described a dynamic programming algorithm to enumerate all locally optimal structures. In this paper, we apply methods from enumerative combinatorics to compute the asymptotic number of such structures. Additionally, we consider analogous combinatorial problems for secondary structures with annotated single-stranded, stacking nucleotides (dangles).     

Experimental design for optimal parameter estimation of an enzyme kinetic process based on the analysis of the Fisher information matrix

The investigation of enzyme kinetics is increasingly important, especially for finding active substances and understanding intracellular behaviors. Therefore, the determination of an enzyme's kinetic parameters is crucial. For this a systematic experimental design procedure is necessary to avoid wasting time and resources. The parameter estimation error of a Michaelis-Menten enzyme kinetic process is analysed analytically to reduce the search area as well as numerically to specify the optimum for parameter estimation. From analytical analysis of the Fisher information matrix the fact is obtained, that an enzyme feed will not improve the estimation process, but substrate feeding is favorable with small volume flow. Unconstrained and constrained process conditions are considered. If substrate fed-batch process design is used instead of pure batch experiments the improvements of the Cramer-Rao lower bound of the variance of parameter estimation error reduces to 82% for mu(max) and to 60% for K(m) of the batch values in average.     

Plastic value chain and performance metric framework for optimal recycling

Despite the promotion of plastic recycling to sustainably manage plastic waste and advance the circular economy, existing plastic recycling systems globally are largely experiencing low performance and growth. To transition to world-class plastic material recycling and circularity, defining the metrics that impact the performance of a plastic recycling system is crucial. Bringing together existing literature, this study developed a conceptual framework, comprised of eight key performance metrics, for benchmarking recycling success or assessing the degree to which the performance of any plastic recycling system is optimal. Through a value chain approach, the specific performance metrics relevant to each stage of the plastic recycling system, their objectives, and the actors characterizing the system were analyzed in detail. Also, specific maturity models were developed to measure the performance of any plastic recycling system. This framework provides essential knowledge for related stakeholders to inform further development of plastic recycling and a circular economy.     

Signal transduction: receptor clusters as information processing arrays

The organization of transmembrane receptors into higher-order arrays occurs in cells as different as bacteria, lymphocytes and neurons. What are the implications of receptor clustering for short-term and long-term signaling processes that occur in response to ligand binding?     

Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model

Journal of Computational Neuroscience - In vitro studies have shown that hippocampal pyramidal neurons employ a mechanism similar to stochastic resonance (SR) to enhance the detection and...     

Myofibril orientation as a metric for characterizing heart disease

Myocyte disarray is a hallmark of many cardiac disorders. However, the relationship between alterations in the orientation of individual myofibrils and myofilaments to disease progression has been largely underexplored. This oversight has predominantly been because of a paucity of methods for objective and quantitative analysis. Here, we introduce a novel, less-biased approach to quantify myofibrillar and myofilament orientation in cardiac muscle under near-physiological conditions and demonstrate its superiority as compared with conventional histological assessments. Using small-angle x-ray diffraction, we first investigated changes in myofibrillar orientation at increasing sarcomere lengths in permeabilized, relaxed, wild-type mouse myocardium from the left ventricle by assessing the angular spread of the 1,0 equatorial reflection (angle σ). At a sarcomere length of 1.9 μm, the angle σ was 0.23 ± 0.01 rad, decreased to 0.19 ± 0.01 rad at a sarcomere length of 2.1 μm, and further decreased to 0.15 ± 0.01 rad at a sarcomere length of 2.3 μm (p < 0.0001). Angle σ was significantly larger in R403Q, a MYH7 hypertrophic cardiomyopathy model, porcine myocardium (0.24 ± 0.01 rad) compared with wild-type myocardium (0.14 ± 0.005 rad; p < 0.0001), as well as in human heart failure tissue (0.19 ± 0.006 rad) when compared with nonfailing samples (0.17 ± 0.007 rad; p = 0.01). These data indicate that diseased myocardium suffers from greater myofibrillar disorientation compared with healthy controls. Finally, we showed that conventional, histology-based analysis of disarray can be subject to user bias and/or sampling error and lead to false positives. Our method for directly assessing myofibrillar orientation avoids the artifacts introduced by conventional histological approaches that assess myocyte orientation and only indirectly evaluate myofibrillar orientation, and provides a precise and objective metric for phenotypically characterizing myocardium. The ability to obtain excellent x-ray diffraction patterns from frozen human myocardium provides a new tool for investigating structural anomalies associated with cardiac diseases.     

A general model of stochastic neural processing

A model of neural processing is proposed which is able to incorporate a great deal of neurophysiological detail, including effects associated with the mechanics of postsynaptic summation and cell surface geometry and is capable of hardware realisation as a probabilistic random access memory (pRAM). The model is an extension of earlier work by the authors, which by operating at much shorter time scales (of the order of the lifetime of a quantum of neurotransmitter in the synaptic cleft) allows a greater amount of information to be retrieved from the simulated spike train. The mathematical framework for the model appears to be that of an extended Markov process (involving the firing histories of the N neurons); simulation work has yielded results in excellent agreement with theoretical predictions. The extended neural model is expected to be particularly applicable in situations where timing constraints are of special importance (such as the auditory cortex) or where firing thresholds are high, as in the case for the granule and pyramidal cells of the hippocampus.     

Didactic discussion of stochastic resonance effects and weak signals

A simple, paradigmatic, model is used to illustrate some general properties of effects subsumed under the label “stochastic resonance.” In particular, analyses of the transparent model show that 1) a small amount of noise added to a much larger signal can greatly increase the response to the signal, but 2) a weak signal added to much larger noise will not generate a substantial added response. The conclusions drawn from the model illustrate the general result that stochastic resonance effects do not provide an avenue for signals that are much smaller than noise to affect biology. A further analysis demonstrates the effects of small signals in the shifting of biologically important chemical equilibria under conditions where stochastic resonance effects are significant. © 1996 Wiley-Liss, Inc.     

An evaluation of information content as a metric for the inference of putative conserved noncoding regions in DNA sequences using a genetic algorithms approach

In previous work, we presented GAMI, an approach to motif inference that uses a genetic algorithms search. GAMI is designed specifically to find putative conserved regulatory motifs in noncoding regions of divergent species, and is designed to allow for analysis of long nucleotide sequences. In this work, we compare GAMI's performance when run with its original fitness function (a simple count of the number of matches) and when run with information content, as well as several variations on these metrics. Results indicate that information content does not identify highly conserved regions, and thus is not the appropriate metric for this task, while variations on information content as well as the original metric succeed in identifying putative conserved regions.