Dynamic random noise shrinks the twinkling aftereffect induced by artificial scotomas

AbstractPhysiological alterations in cortical neurons are induced during adaptation to an artificial scotoma, a small homogeneous patch within a dynamic random noise or patterned background. When the dynamic noise is replaced by an equiluminant gray background, a twinkling aftereffect can be seen in the location of the artificial scotoma. Following binocular adaptation, we discovered that the perceived size of the twinkling aftereffect was dramatically smaller than the inducing artificial scotoma. Dichoptic adaptation induced shrinkage in the twinkling aftereffect that was similar to that found after binocular adaptation, suggesting that the twinkling aftereffect and its shrinkage both have cortical origins. We speculate that this perceptual shrinkage may reflect the interaction between two cortical mechanisms: a twinkling aftereffect mechanism that spreads throughout the artificial scotoma, and a filling-in mechanism that has a greater influence at the edges of the artificial scotoma and spreads inwards

Adaptation to Noise in Human Speech Recognition Unrelated to the Medial Olivocochlear Reflex.

[EN]Sensory systems constantly adapt their responses to the current environment. In hearing, adaptation may facilitate communication in noisy settings, a benefit frequently (but controversially) attributed to the medial olivocochlear reflex (MOCR) enhancing the neural representation of speech. Here, we show that human listeners (N = 14; five male) recognize more words presented monaurally in ipsilateral, contralateral, and bilateral noise when they are given some time to adapt to the noise. This finding challenges models and theories that claim that speech intelligibility in noise is invariant over time. In addition, we show that this adaptation to the noise occurs also for words processed to maintain the slow-amplitude modulations in speech (the envelope) disregarding the faster fluctuations (the temporal fine structure). This demonstrates that noise adaptation reflects an enhancement of amplitude modulation speech cues and is unaffected by temporal fine structure cues. Last, we show that cochlear implant users (N = 7; four male) show normal monaural adaptation to ipsilateral noise. Because the electrical stimulation delivered by cochlear implants is independent from the MOCR, this demonstrates that noise adaptation does not require the MOCR. We argue that noise adaptation probably reflects adaptation of the dynamic range of auditory neurons to the noise level statistics.SIGNIFICANCE STATEMENT People find it easier to understand speech in noisy environments when they are given some time to adapt to the noise. This benefit is frequently but controversially attributed to the medial olivocochlear efferent reflex enhancing the representation of speech cues in the auditory nerve. Here, we show that the adaptation to noise reflects an enhancement of the slow fluctuations in amplitude over time that are present in speech. In addition, we show that adaptation to noise for cochlear implant users is not statistically different from that for listeners with normal hearing. Because the electrical stimulation delivered by cochlear implants is independent from the medial olivocochlear efferent reflex, this demonstrates that adaptation to noise does not require this reflex

Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks

We propose an adaptive diffusion mechanism to optimize a global cost function in a distributed manner over a network of nodes. The cost function is assumed to consist of a collection of individual components. Diffusion adaptation allows the nodes to cooperate and diffuse information in real-time; it also helps alleviate the effects of stochastic gradient noise and measurement noise through a continuous learning process. We analyze the mean-square-error performance of the algorithm in some detail, including its transient and steady-state behavior. We also apply the diffusion algorithm to two problems: distributed estimation with sparse parameters and distributed localization. Compared to well-studied incremental methods, diffusion methods do not require the use of a cyclic path over the nodes and are robust to node and link failure. Diffusion methods also endow networks with adaptation abilities that enable the individual nodes to continue learning even when the cost function changes with time. Examples involving such dynamic cost functions with moving targets are common in the context of biological networks.Comment: 34 pages, 6 figures, to appear in IEEE Transactions on Signal Processing, 201

Chemotactic response and adaptation dynamics in Escherichia coli

Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia coli is integral for detecting chemicals over a wide range of background concentrations, ultimately allowing cells to swim towards sources of attractant and away from repellents. Its biochemical mechanism based on methylation and demethylation of chemoreceptors has long been known. Despite the importance of adaptation for cell memory and behavior, the dynamics of adaptation are difficult to reconcile with current models of precise adaptation. Here, we follow time courses of signaling in response to concentration step changes of attractant using in vivo fluorescence resonance energy transfer measurements. Specifically, we use a condensed representation of adaptation time courses for efficient evaluation of different adaptation models. To quantitatively explain the data, we finally develop a dynamic model for signaling and adaptation based on the attractant flow in the experiment, signaling by cooperative receptor complexes, and multiple layers of feedback regulation for adaptation. We experimentally confirm the predicted effects of changing the enzyme-expression level and bypassing the negative feedback for demethylation. Our data analysis suggests significant imprecision in adaptation for large additions. Furthermore, our model predicts highly regulated, ultrafast adaptation in response to removal of attractant, which may be useful for fast reorientation of the cell and noise reduction in adaptation.Comment: accepted for publication in PLoS Computational Biology; manuscript (19 pages, 5 figures) and supplementary information; added additional clarification on alternative adaptation models in supplementary informatio

RDGSL: Dynamic Graph Representation Learning with Structure Learning

Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline

Perceptual response to visual noise and display media

The present project was designed to follow up an earlier investigation in which perceptual adaptation in response to the use of Night Vision Goggles, or image intensification (I squared) systems, such as those employed in the military were studied. Our chief concern in the earlier studies was with the dynamic visual noise that is a byproduct of the I(sup 2) technology: under low light conditions, there is a great deal of 'snow' or sporadic 'twinkling' of pixels in the I(sup 2) display which is more salient as the ambient light levels are lower. Because prolonged exposure to static visual noise produces strong adaptation responses, we reasoned that the dynamic visual noise of I(sup 2) displays might have a similar effect, which could have implications for their long term use. However, in the series of experiments reported last year, no evidence at all of such aftereffects following extended exposure to I(sup 2) displays were found. This finding surprised us, and led us to propose the following studies: (1) an investigation of dynamic visual noise and its capacity to produce after effects; and (2) an investigation of the perceptual consequences of characteristics of the display media

Robust ASR using Support Vector Machines

The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important shortcomings have had to be circumvented, the most important being the normalisation of the time duration of different realisations of the acoustic speech units. In this paper, we have compared two approaches in noisy environments: first, a hybrid HMM–SVM solution where a fixed number of frames is selected by means of an HMM segmentation and second, a normalisation kernel called Dynamic Time Alignment Kernel (DTAK) first introduced in Shimodaira et al. [Shimodaira, H., Noma, K., Nakai, M., Sagayama, S., 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In: Proc. Eurospeech, Aalborg, Denmark, pp. 1841–1844] and based on DTW (Dynamic Time Warping). Special attention has been paid to the adaptation of both alternatives to noisy environments, comparing two types of parameterisations and performing suitable feature normalisation operations. The results show that the DTA Kernel provides important advantages over the baseline HMM system in medium to bad noise conditions, also outperforming the results of the hybrid system.Publicad