Signal denoising through topographic modularity of neural circuits

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

Exploration of Effective Attention Strategies for Neural Automatic Post-editing with Transformer

Automatic post-editing (APE) is the study of correcting translation errors in the output of an unknown machine translation (MT) system and has been considered as a method of improving translation quality without any modification to conventional MT systems. Recently, several variants of Transformer that take both the MT output and its corresponding source sentence as inputs have been proposed for APE; and models introducing an additional attention layer into the encoder to jointly encode the MT output with its source sentence recorded a high-rank in the WMT19 APE shared task. We examine the effectiveness of such joint-encoding strategy in a controlled environment and compare four types of decoder multi-source attention strategies that have been introduced into previous APE models. The experimental results indicate that the joint-encoding strategy is effective and that taking the final encoded representation of the source sentence is the more proper strategy than taking such representation within the same encoder stack. Furthermore, among the multi-source attention strategies combined with the joint-encoding, the strategy that applies attention to the concatenated input representation and the strategy that adds up the individual attention to each input improve the quality of APE results over the strategy using the joint-encoding only.

The effects of combined cognitive training on prospective memory in older adults with mild cognitive impairment

This study aimed to assess the effects of combined cognitive training on prospective memory ability of older adults with mild cognitive impairment (MCI). A total of 113 participants were divided into a control group and three intervention groups. Over three months, the control group received only community education without any training, whereas for the first six weeks, an executive function training group received executive function training, a memory strategy training group received semantic encoding strategy training, and the combined cognitive training group received executive function training twice a week for the first six weeks, and semantic encoding strategy training twice a week for the next six weeks. The combined cognitive training group showed improvement on the objective neuropsychological testing (Montreal Cognitive Assessment scale). The memory strategy training group showed improvement on the self-evaluation scales (PRMQ-PM). Combined cognitive training improved the prospective memory and cognitive function of older adults with MCI.

An Intra-Prediction Mode-Based Video Steganography With Secure Strategy

In this paper, an intra-prediction mode (IPM)-based video steganography with secure strategy was proposed for H.264 video stream. First of all, according to the property of IPM conversion after calibration, a content-adaptive selection strategy was adopted to measure candidate carrier macroblock. Then, a more efficient encoding strategy based on grouped IPM was applied to encode secret message. This encoding strategy aimed to further enhance the security performance by exploiting the deviation feature of calibrated IPM. Finally, syndrome-trellis code was used as the embedding implementation to minimize distortion. Experimental results demonstrate that this article proposed algorithm presents a novel security performance with any existing IPM-based video steganography.

Modulation Ranges of Different Sensations for Coding Electrically Evoked Tactile Sensory Feedback

The ability to perceive prosthetic grasping may enable amputees to better interact with external objects. This may require customized coding of multiple sensory feedback for each amputee. This study developed a protocol to determine optimal modulation ranges of sensations elicited by transcutaneous electrical nerve stimulation (TENS). These sensations that were referred to the lost fingers provided the possibility for restoring multi-modalities of sensory feedback for amputees with evoked tactile sensation (ETS) non-invasively. To match the restricted projected finger map area, smaller electrodes must be used to deliver electrical stimulation for multi-channel sensory information, which resulted in fewer types of sensations. Our protocol provided comprehensive information for optimal selection of amplitude and frequency in a personalized, pulse-width encoding paradigm. The good sensitivity for vibration and buzz in both able-bodied and amputee subjects suggested that perceptual intensity can be effectively modulated to convey sensory information via either of the sensations. The efficacy of this protocol in sensory coding for forearm amputees was demonstrated in finger-specific identification experiment. This protocol may allow customization of ETS-based sensory feedback with an optimal encoding strategy for individual amputees.

Modulation Ranges of Different Sensations for Coding Electrically Evoked Tactile Sensory Feedback

The ability to perceive prosthetic grasping may enable amputees to better interact with external objects. This may require customized coding of multiple sensory feedback for each amputee. This study developed a protocol to determine optimal modulation ranges of sensations elicited by transcutaneous electrical nerve stimulation (TENS). These sensations that were referred to the lost fingers provided the possibility for restoring multi-modalities of sensory feedback for amputees with evoked tactile sensation (ETS) non-invasively. To match the restricted projected finger map area, smaller electrodes must be used to deliver electrical stimulation for multi-channel sensory information, which resulted in fewer types of sensations. Our protocol provided comprehensive information for optimal selection of amplitude and frequency in a personalized, pulse-width encoding paradigm. The good sensitivity for vibration and buzz in both able-bodied and amputee subjects suggested that perceptual intensity can be effectively modulated to convey sensory information via either of the sensations. The efficacy of this protocol in sensory coding for forearm amputees was demonstrated in finger-specific identification experiment. This protocol may allow customization of ETS-based sensory feedback with an optimal encoding strategy for individual amputees.

Rat sensitivity to multipoint statistics is predicted by efficient coding of natural scenes

Efficient processing of sensory data requires adapting the neuronal encoding strategy to the statistics of natural stimuli. Humans, for instance, are most sensitive to multipoint correlations that vary the most across natural images. Here we show that rats possess the same sensitivity ranking to multipoint statistics as humans, thus extending a classic demonstration of efficient coding to a species where neuronal and developmental processes can be interrogated and causally manipulated.

Improving the Reversible LSB Matching Scheme Based on the Likelihood Re-encoding Strategy

In 2018, Tseng et al. proposed a dual-image reversible embedding method based on the modified Least Significant Bit matching (LSB matching) method. This method improved on the dual-image LSB matching method proposed by Lu et al. In Lu et al.’s scheme, there are seven situations that cannot be restored and need to be modified. Furthermore, the scheme uses two pixels to conceal four secret bits. The maximum modification of each pixel, in Lu et al.’s scheme, is two. To decrease the modification, Tseng et al. use one pixel to embed two secret bits and allow the maximum modification to decrease from two to one such that the image quality can be improved. This study enhances Tseng et al.’s method by re-encoding the modified rule table based on the probability of each hiding combination. The scheme analyzes the frequency occurrence of each combination and sets the lowest modified codes to the highest frequency case to significantly reduce the amount of modification. Experimental results show that better image quality is obtained using our method under the same amount of hiding payload.