The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform

Vadim V. Nikulin - One of the best experts on this subject based on the ideXlab platform.

  • spontaneous neural oscillations influence behavior and sensory representations by suppressing neuronal excitability
    bioRxiv, 2021
    Co-Authors: Luca Iemi, Jason Samaha, Vadim V. Nikulin, L Gwilliams, Ryszard Auksztulewicz, Y M Cycowicz, J King, Thomas Thesen, Werner Doyle, Orrin Devinsky
    Abstract:

    The ability to process and respond to external input is critical for adaptive behavior. Why, then, do neural and behavioral responses vary across repeated presentations of the same sensory input? Spontaneous fluctuations of neuronal excitability are currently hypothesized to underlie the trial-by-trial variability in sensory processing. To test this, we capitalized on invasive electrophysiology in neurosurgical patients performing an auditory discrimination task with visual cues: specifically, we examined the interaction between prestimulus alpha oscillations, excitability, task performance, and decoded neural stimulus representations. We found that strong prestimulus oscillations in the alpha+ band (i.e., alpha and neighboring frequencies), rather than the Aperiodic Signal, correlated with a low excitability state, indexed by reduced broadband high-frequency activity. This state was related to slower reaction times and reduced neural stimulus encoding strength. We propose that the alpha+ rhythm modulates excitability, thereby resulting in variability in behavior and sensory representations despite identical input.

  • Multiple mechanisms link prestimulus neural oscillations to sensory responses.
    eLife, 2019
    Co-Authors: Luca Iemi, Niko A. Busch, Annamaria Laudini, Saskia Haegens, Jason Samaha, Arno Villringer, Vadim V. Nikulin
    Abstract:

    Spontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha- and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f Aperiodic Signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information.

E. L. Hines - One of the best experts on this subject based on the ideXlab platform.

  • Stimulus reconstruction from neural spike trains: are conventional filters suitable for both periodic and Aperiodic stimuli?
    Signal Processing, 2006
    Co-Authors: Aruneema Das, N. G. Stocks, R. Folland, E. L. Hines
    Abstract:

    Human neurone system encodes all stimuli into series of spike trains and our brain reconstructs the stimulus back from the spikes. Main purpose of this paper is to identify the suitability of conventional filters to mimic the process of original stimulus reconstruction from neural spike trains as brain does. As human brain receives periodic and Aperiodic types of Signals, in this paper we have used pulse oximetry waveforms (periodic) and Gaussian Signal (Aperiodic) as the stimuli. For the neural spike train generation two different neurone models have been used, one model was very simple single threshold level crossing detector and the other one was an advanced simulated stochastic leaky integrate-and-fire neurone model with dynamical threshold. Level crossing detector model is used to generate spike trains from periodic Signal whereas both neurone models have been used to generate spike trains for Aperiodic Gaussian Signal. A simple low-pass Butterworth filter and an advanced Wiener Kolmogorov filter have been used for reconstructing the stimuli from spike trains. Comparison of the results has been done by the measure of cross correlation coefficients between the actual stimulus and the reconstructed counterpart. Comparison of the results reveal that simple level crossing detector model along with an advanced Wiener-Kolmogorov filter can achieve reconstruction of periodic Signal up to 98.91% whereas combination of the advanced simulated neurone models with an advanced Wiener-Kolmogorov filter does not achieve reconstruction of more than 55% for Aperiodic Signal. This study proves that conventional filters are good for periodic Signal reconstruction from their neural spike trains but they are not suitable for Aperiodic Signals.

  • Quantifying stochastic resonance in a single threshold detector for random Aperiodic Signals
    Fluctuation and Noise Letters, 2004
    Co-Authors: Aruneema Das, N. G. Stocks, A. Nikitin, E. L. Hines
    Abstract:

    We explore stochastic resonance (SR) effects in a single comparator (threshold detector) driven by either a Gaussian or exponentially distributed Aperiodic Signal. The behaviour of different performance measures, namely the cross-correlation coefficient (CCC), Signal-to-noise ratio (SNR) and mutual information, I, has been investigated. The Signals were added to Gaussian noise before being passed through the threshold detector. For the two Signals tested, we observe the perhaps surprising result that the SNR never displays SR. However, SR is displayed by both the CCC and I for Gaussian Signals. For exponential Signals SR is not displayed by any of the measures. By generating Signals whose probability distributions have the generalized Gaussian form Ae-|βx|n it is possible to demonstrate that SR ceases to occur if n

  • QUANTIFYING STOCHASTIC RESONANCE IN A SINGLE THRESHOLD DETECTOR FOR RANDOM Aperiodic SignalS
    Fluctuation and Noise Letters, 2004
    Co-Authors: Aruneema Das, N. G. Stocks, A. Nikitin, E. L. Hines
    Abstract:

    We explore stochastic resonance (SR) effects in a single comparator (threshold detector) driven by either a Gaussian or exponentially distributed Aperiodic Signal. The behaviour of different performance measures, namely the cross-correlation coefficient (CCC), Signal-to-noise ratio (SNR) and mutual information, I, has been investigated. The Signals were added to Gaussian noise before being passed through the threshold detector. For the two Signals tested, we observe the perhaps surprising result that the SNR never displays SR. However, SR is displayed by both the CCC and I for Gaussian Signals. For exponential Signals SR is not displayed by any of the measures. By generating Signals whose probability distributions have the generalized Gaussian form Ae-|βx|n it is possible to demonstrate that SR ceases to occur if n<1.7. We conclude that SR is only observable in threshold based systems for certain types of Aperiodic Signal. Specifically, SR is not expected to occur for Signals whose probability density functions have long, slowly decaying, tails. We discuss the implication of these results for the role of SR in biological sensory systems.

Hu Gang - One of the best experts on this subject based on the ideXlab platform.

François Chapeau-blondeau - One of the best experts on this subject based on the ideXlab platform.

  • Noise-enhanced transmission efficacy of Aperiodic Signals in nonlinear systems
    International Journal of Modern Physics: Conference Series, 2014
    Co-Authors: Fabing Duan, François Chapeau-blondeau, Derek Abbott
    Abstract:

    We study the Aperiodic Signal transmission in a static nonlinearity in the context of Aperiodic stochastic resonance. The performance of a nonlinearity over that of the linear system is defined as the transmission efficacy. The theoretical and numerical results demonstrate that the noise-enhanced transmission efficacy e occur for different Signal strengths in various noise scenarios.

  • Noise-Enhanced Transmission Efficacy of Aperiodic Signals in Nonlinear Systems
    viXra, 2013
    Co-Authors: Fabing Duan, François Chapeau-blondeau, Derek Abbott
    Abstract:

    We study the Aperiodic Signal transmission in a static nonlinearity in the context of Aperiodic stochastic resonance. The performance of a nonlinearity over that of the linear system is defined as the transmission efficacy. The theoretical and numerical results demonstrate that the noise-enhanced transmission efficacy effects occur for different Signal strengths in various noise scenarios.

  • Noise-enhanced capacity via stochastic resonance in an asymmetric binary channel
    Physical Review E, 1997
    Co-Authors: François Chapeau-blondeau
    Abstract:

    A nonlinear system is considered where an Aperiodic binary input Signal is added to an arbitrarily distributed noise and compared to a fixed threshold to determine the binary output Signal. Noise enhancement of the transmission of the Aperiodic Signal via stochastic resonance is demonstrated and studied in this nonlinear information channel. The characterization developed goes up to the calculation of the information capacity of the channel, defined as the maximal achievable input-output transinformation occurring when the statistics of the input Signal is matched to the noise. It is then demonstrated that a regime exists where the information capacity of the channel can be increased by means of an increase of the noise, up to an optimal noise level where the capacity resonates at a maximum value. The influence on this resonance of the noise distribution is also studied.

Derek Abbott - One of the best experts on this subject based on the ideXlab platform.

  • Noise-enhanced transmission efficacy of Aperiodic Signals in nonlinear systems
    International Journal of Modern Physics: Conference Series, 2014
    Co-Authors: Fabing Duan, François Chapeau-blondeau, Derek Abbott
    Abstract:

    We study the Aperiodic Signal transmission in a static nonlinearity in the context of Aperiodic stochastic resonance. The performance of a nonlinearity over that of the linear system is defined as the transmission efficacy. The theoretical and numerical results demonstrate that the noise-enhanced transmission efficacy e occur for different Signal strengths in various noise scenarios.

  • Noise-Enhanced Transmission Efficacy of Aperiodic Signals in Nonlinear Systems
    viXra, 2013
    Co-Authors: Fabing Duan, François Chapeau-blondeau, Derek Abbott
    Abstract:

    We study the Aperiodic Signal transmission in a static nonlinearity in the context of Aperiodic stochastic resonance. The performance of a nonlinearity over that of the linear system is defined as the transmission efficacy. The theoretical and numerical results demonstrate that the noise-enhanced transmission efficacy effects occur for different Signal strengths in various noise scenarios.

  • Cross-spectral measurement of neural Signal transfer
    Noise in Complex Systems and Stochastic Dynamics II, 2004
    Co-Authors: Mark D. Mcdonnell, Swaminathan Sethuraman, Laszlo B. Kish, Derek Abbott
    Abstract:

    The phenomenon of noise enhanced Signal transfer, or stochastic resonance, has been observed in many nonlinear systems such as neurons and ion channels. Initial studies of stochastic resonance focused on systems driven by a periodic Signal, and hence used a Signal to noise ratio based measure for comparison between the input and output of the system. It has been pointed out that for the more general case of Aperiodic Signals other measures are required, such as cross-correlation or information theoretical tools. In this paper we present simulation results obtained in a model neural system driven by a broadband Aperiodic Signal, and producing a Signal imitating neural spikes. The system is analyzed by using cross-spectral measures.

  • Simulation of circuits demonstrating stochastic resonance
    Microelectronics Journal, 2000
    Co-Authors: Gregory P. Harmer, Derek Abbott
    Abstract:

    Abstract In certain dynamical systems, the addition of noise can assist the detection of a Signal and not degrade it as normally expected. This is possible via a phenomenon termed stochastic resonance (SR), where the response of a nonlinear system to a subthreshold periodic input Signal is optimal for some non-zero value of noise intensity. We investigate the SR phenomenon in several circuits and systems. Although SR occurs in many disciplines, the sinusoidal Signal by itself is not information bearing. To greatly enhance the practicality of SR, an (Aperiodic) broadband Signal is preferable. Hence, we employ Aperiodic stochastic resonance (ASR) where noise can enhance the response of a nonlinear system to a weak Aperiodic Signal. We can characterize ASR by the use of cross-correlation-based measures. Using this measure, the ASR in a simple threshold system and in a FitzHugh–Nagumo neuronal model are compared using numerical simulations. Using both weak periodic and Aperiodic Signals, we show that the response of a nonlinear system is enhanced, regardless of the Signal.

  • Simulation of circuits demonstrating stochastic resonance
    Design Characterization and Packaging for MEMS and Microelectronics, 1999
    Co-Authors: Gregory P. Harmer, Derek Abbott
    Abstract:

    In certain dynamic systems, the addition of nose can assist the detection of a Signal and not degrade it as normally expected. This is possible via a phenomenon termed stochastic resonance (SR). The response of a nonlinear system to a sub-threshold periodic input Signal is optimal for some non-zero value of noise intensity. Using the Signal-to-noise ratio (SNR) we can characterize SR - as the noise increases the SNR rises sharply, which is followed by a gradual decrease. We investigate the SR phenomenon in several circuits and numerical simulations. In particular, the effect that the system linearity has on the amount of gain introduced by SR and the effect of varying the input Signal strength. We demonstrate, for a thresholding system, as much as a 20 dB improvement in SNR, which may be increased by further investigation. Although SR occurs in many disciplines, the sinusoidal Signal itself is not information bearing. To greatly enhance the practical applications of SR, we require operation with an Aperiodic broadband Signal. Hence, we introduce Aperiodic stochastic resonance (ASR) where noise can enhance the response of a nonlinear system to a weak Aperiodic Signal. As the input Signal is Aperiodic, an alternative quantitative measure is required rather than the SNR used with periodic Signals. We can characterize ASR by the use of cross-correlation-based- measures. Using this measure, the ASR in a simple threshold system and in a FitzHugh-Nagumo neuronal model are compared using numerical simulations. Using both weak periodic and Aperiodic Signal we show that the response of a nonlinear system is enhanced, regardless of the Signal.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.