Biomedical subjects

A Neiman

Publications and source records attributed to A Neiman.

At least 19 recordsLinked to original sources

PubMed2001

Entropy and local uncertainty of data from sensory neurons.

We present an empirical comparison between neural interspike interval sequences obtained from two different kinds of sensory receptors. Both differ in their internal structure as well as in the strength of correlations and the degree of predictability found in the respective spike trains. As a further tool in this context, we suggest the local uncertainty, assigning a well-defined predictability to individual spikes. The local uncertainty is demonstrated to reveal significant patterns within the interspike interval sequences, even when its overall structure is (almost) random. Our approach is based on the concept of symbolic dynamics and information theory.

Animals↗

PubMed2001

Noise-induced phase synchronization enhanced by dichotomic noise.

We study the nonlinear response of a stochastic bistable system driven by both a weak periodic signal and a dichotomic noise in terms of stochastic phase synchronization. We show that the effect of noise-induced phase synchronization can be significantly enhanced by the addition of a dichotomic noise.

Journal Article↗

PubMed2001

Stochastic biperiodic oscillations in the electroreceptors of paddlefish.

We report that the electroreceptors in paddlefish possess the novel property of being biperiodic, that is, being composed of two intrinsic self-sustained noisy oscillators, one residing in the hair cells, and another in the terminals of primary afferent neurons. The two oscillators are coupled unidirectionally. Thus the receptor system as a whole undergoes stochastic biperiodic oscillations. We characterize the spontaneous activity of this system of coupled biological oscillators, and also discuss the impact of the biperiodic organization on the transduction of external sensory stimuli. In particular, we show that the existence of hair cell oscillations leads to additional variability of afferent spike trains.

Action Potentials↗

PubMed2001

Behavioral stochastic resonance: how a noisy army betrays its outpost.

Juvenile paddlefish prey upon single zooplankton by detecting a weak electric signature resulting from its feeding and swimming motions. Moreover, it has recently been shown that paddlefish make use of stochastic resonance near the threshold for prey detection: a process termed behavioral stochastic resonance. But this process depends upon an external source of electric noise. A swarm of plankton, for example, Daphnia, can provide this noise. Assuming that juvenile paddlefish attack single Daphnia as outliers in the vicinity of the swarm, making use of noise from the swarm, we calculate the spatial distribution of the average phase locking period for the subthreshold signals acting at the paddlefish rostrum. Numeric evaluation of analytic formulas supports the notion of a noise-induced widening of the capture area quantitatively.

Algorithms↗

PubMed2000

Stochastic resonance enhances the electrosensory information available to paddlefish for prey capture.

Recent behavior experiments have demonstrated that paddlefish can make use of stochastic resonance while feeding on Daphnia plankton. Here we calculate the information content of the noisy Daphnia signal at the paddlefish rostrum using an exact statistical treatment of threshold stochastic resonance as a minimal neural model. These calculations compare well with experimentally obtained data on paddlefish strikes at Daphnia prey.

Animals↗

PubMed2000

Detecting the onset of bifurcations and their precursors from noisy data

We study the problem of the detection of noise-induced precursors of periodic motion instabilities in stochastic dynamical systems. In particular, we concentrate on the period-doubling bifurcation. We have developed a statistical method to detect the onset of bifurcations and their precursors based on the previously established topological recurrence technique.

Journal Article↗

PubMed2000

Stochastic resonance enhanced by dichotomic noise in a bistable system

We study linear responses of a stochastic bistable system driven by dichotomic noise to a weak periodic signal. We show that the effect of stochastic resonance can be greatly enhanced in comparison with the conventional case when dichotomic forcing is absent, that is, both the signal-to-noise ratio and the spectral power amplification reach much greater values than in the standard stochastic resonance setup.

Journal Article↗

PubMed2000

BUR1 and BUR2 encode a divergent cyclin-dependent kinase-cyclin complex important for transcription in vivo.

BUR1 and BUR2 were previously identified by a genetic selection for mutations that increase transcription from basal promoters in vivo. BUR1 encoded a putative protein kinase with greatest similarity to members of the cyclin-dependent kinase (CDK) family, although that similarity was not sufficient to classify it as a CDK. It was also not known whether Bur1 activity was cyclin dependent and, if so, which cyclins stimulated Bur1. The molecular cloning and characterization of BUR2 presented here sheds light on these issues. Genetic analysis indicates that BUR2 function is intimately related to that of BUR1: bur1 and bur2 mutations cause nearly identical spectra of mutant phenotypes, and overexpression of BUR1 suppresses a bur2 null allele. Biochemical analysis has provided a molecular basis for these genetic observations. We find that BUR2 encodes a cyclin for the Bur1 protein kinase, based on the following evidence. First, the BUR2 amino acid sequence reveals similarity to the cyclins; second, Bur1 and Bur2 coimmunoprecipitate from crude extracts and interact in the two-hybrid system; and third, BUR2 is required for Bur1 kinase activity in vitro. Our combined genetic and biochemical results therefore indicate that Bur1 and Bur2 comprise a divergent CDK-cyclin complex that has an important functional role during transcription in vivo.

Alleles↗

PubMed1999

Surrogates for finding unstable periodic orbits in noisy data sets.

Recently, searches for unstable periodic orbits in biological and medical applications have become of interest. The motivations for this research range, in order of ascending complexity, from efforts to understand the dynamics of simple sensory neurons, through speculations regarding neural coding, to the hopeful development of new diagnostic and/or control techniques for cardiac and epileptic pathologies. Biological and medical data are, however, noisy and nonstationary. Findings of unstable periodic orbits in such data thus require convincing assessments of their statistical significance. Such tests are accomplished by comparison with surrogate data files designed to test an appropriate null hypothesis. In this paper we test surrogates generated by three different algorithms against correlated noise as well as stable periodic orbits. One of the surrogates is new, and has been specifically designed to preserve the shape of the attractor. We discuss the suitability of these surrogates and argue that the simple shuffled one correctly tests the appropriate null hypothesis.

Journal Article↗

PubMed1999

Synchronization of noisy systems by stochastic signals.

We study, in terms of synchronization, the nonlinear response of noisy bistable systems to a stochastic external signal, represented by Markovian dichotomic noise. We propose a general kinetic model which allows us to conduct a full analytical study of the nonlinear response, including the calculation of cross-correlation measures, the mean switching frequency, and synchronization regions. Theoretical results are compared with numerical simulations of a noisy overdamped bistable oscillator. We show that dichotomic noise can instantaneously synchronize the switching process of the system. We also show that synchronization is most pronounced at an optimal noise level-this effect connects this phenomenon with aperiodic stochastic resonance. Similar synchronization effects are observed for a stochastic neuron model stimulated by a stochastic spike train.

Journal Article↗

PubMed1996