Processing Mechanisms Research Articles

Temporal integration on the dendrites of fast-spiking basket cells

Neurons receive synaptic inputs with diverse temporal patterns in vivo, and their integration of these patterns is critical for understanding information processing mechanisms in the brain. Fast-spiking basket cells, which perform both supralinear and sublinear dendritic integration, are essential for inhibitory control in the hippocampus. However, their responses and the mechanisms underlying different temporal input patterns remain unclear. To address this question, we apply inputs with varying windows of time to a detailed compartmental model of basket cells. Our results reveal that when synaptic inputs are randomly dispersed, temporal integration in FS BCs exhibits a sigmoid-like response within the temporal window. In contrast, synchronous input protocols more effectively elicit action potentials, while asynchronous inputs generate more spikes in response to suprathreshold stimuli. Further analysis shows that the supralinear dendrites of fast-spiking basket cells primarily mediate this nonlinearity to asynchronous inputs, owing to their larger dendritic diameters. Moreover, we discover that delayed rectifier channels reduce sensitivity to synchronous inputs, whereas N-type channels enhance sensitivity to asynchronous inputs. These results provide insights into the mechanisms underlying the temporal coding of fast-spiking basket cells, which is crucial for understanding their role in neuronal oscillations.

Revolutionizing healthcare: a comparative insight into deep learning’s role in medical imaging

Recently, Deep Learning (DL) models have shown promising accuracy in analysis of medical images. Alzeheimer Disease (AD), a prevalent form of dementia, uses Magnetic Resonance Imaging (MRI) scans, which is then analysed via DL models. To address the model computational constraints, Cloud Computing (CC) is integrated to operate with the DL models. Recent articles on DL-based MRI have not discussed datasets specific to different diseases, which makes it difficult to build the specific DL model. Thus, the article systematically explores a tutorial approach, where we first discuss a classification taxonomy of medical imaging datasets. Next, we present a case-study on AD MRI classification using the DL methods. We analyse three distinct models-Convolutional Neural Networks (CNN), Visual Geometry Group 16 (VGG-16), and an ensemble approach-for classification and predictive outcomes. In addition, we designed a novel framework that offers insight into how various layers interact with the dataset. Our architecture comprises an input layer, a cloud-based layer responsible for preprocessing and model execution, and a diagnostic layer that issues alerts after successful classification and prediction. According to our simulations, CNN outperformed other models with a test accuracy of 99.285%, followed by VGG-16 with 85.113%, while the ensemble model lagged with a disappointing test accuracy of 79.192%. Our cloud Computing framework serves as an efficient mechanism for medical image processing while safeguarding patient confidentiality and data privacy.

Feature selectivity and invariance in marsupial primary visual cortex.

A fundamental question in sensory neuroscience revolves around how neurons represent complex visual stimuli. In mammalian primary visual cortex (V1), neurons decode intricate visual features to identify objects, with most being selective for edge orientation, but with half of those also developing invariance to edge position within their receptive fields. Position invariance allows cells to continue to code an edge even when it moves around. Combining feature selectivity and invariance is integral to successful object recognition. Considering the marsupial-eutherian divergence 160million years ago, we explored whether feature selectivity and invariance was similar in marsupials and eutherians. We recovered the spatial filters and non-linear processing characteristics of the receptive fields of neurons in wallaby V1 and compared them with previous results from cat cortex. We stimulated the neurons in V1 with white Gaussian noise and analysed responses using the non-linear input model. Wallabies exhibit the same high percentage of orientation selective neurons as cats. However, in wallabies we observed a notably higher prevalence of neurons with three or more filters compared to cats. We show that having three or more filters substantially increases phase invariance in the V1s of both species, but that wallaby V1 accentuates this feature, suggesting that the species condenses more processing into the earliest cortical stage. These findings suggest that evolution has led to more than one solution to the problem of creating complex visual processing strategies. KEY POINTS: Previous studies have shown that the primary visual cortex (V1) in mammals is essential for processing complex visual stimuli, with neurons displaying selectivity for edge orientation and position. This research explores whether the visual processing mechanisms in marsupials, such as wallabies, are similar to those in eutherian mammals (e.g. cats). The study found that wallabies have a higher prevalence of neurons with multiple spatial filters in V1, indicating more complex visual processing. Using a non-linear input model, we demonstrated that neurons with three or more filters increase phase invariance. These findings suggest that marsupials and eutherian mammals have evolved similar strategies for visual processing, but marsupials have condensed more capacity to build phase invariance into the first step in the cortical pathway.

A New Approach to the Quality Determination of Used Palm Cooking Oil using Supervised Learning based on Electronic Sensors

As discarding used palm oil in nature is very dangerous, a processing mechanism is needed to utilize it according to needs. This utilization depends on the palm oil used, so the sorting process becomes important. This study proposes a new classification approach for the quality of used palm oil using Self-Organizing Map (SOM), Linear Vector Quantization (LVQ), and K-means, based on electronic sensors. This study included hardware design, software development, data collection, and training and testing processes. Based on the experimental results, the proposed system performed well using 13 parameters consisting of e-nose data, color, viscosity, and turbidity. The accuracy of SOM was 91.11%, LVQ achieved 95.56%, and K-Means obtained an accuracy of 98.89%. This system can be used as a decision support system in the automatic recognition of used palm oil to classify its quality.

Deep feature synthesis approach using selective graph attention for replay attack voice spoofing detection

<span lang="EN-US">As voice-based authentication becomes increasingly integrated into security frameworks, establishing effective defenses against voice spoofing, particularly replay attacks, is more crucial than ever. This paper presents a novel comprehensive framework for replay attack detection that leverages the integration of advanced spectral-temporal feature extraction and graph-based feature processing mechanisms. The proposed system presents the design of a waveform encoder and a novel temporal residual unit for spectral and temporal feature extraction in synchronous. Further, an approach of selective attention graph followed by multi-scale feature synthesis is employed to retain precise and spoof indicative feature vectors at the classification layer. The proposed method addresses the significant challenge of distinguishing genuine speech from replayed recordings. The validation of the proposed model is done on the ASVSpoof2019 dataset to demonstrate the efficacy of the proposed approach. The proposed system outperforms existing methods, achieving a lower equal error rate (EER) of 0.015 and a reduced tandem detection cost function (t-DCF) of 0.503. The comparative outcome exhibits the robustness of the method in identifying replay attacks.</span>

Real-time Detection of Abnormal Financial Transactions Using Generative Adversarial Networks: An Enterprise Application

This paper presents a novel real-time financial fraud detection framework utilizing Generative Adversarial Networks (GANs) for enterprise applications. The proposed system addresses critical challenges in fraud detection, including class imbalance, real-time processing requirements, and enterprise scalability. Implementing a sophisticated multi-layered architecture, the system integrates advanced preprocessing techniques with an optimized GAN model explicitly designed for fraud pattern recognition. The framework incorporates parallel processing capabilities and adaptive batch processing mechanisms to maintain high throughput while ensuring sub-second latency. The experimental evaluation uses a subset of the European Credit Card Transaction dataset, comprising 50,000 transactions with a balanced representation achieved through strategic sampling and SMOTE technique. The proposed model achieves 97.8% accuracy, 96.5% precision, and 95.8% recall, demonstrating competitive performance compared to traditional machine learning approaches. Real-time performance analysis shows consistent sub-100ms latency while maintaining robust performance under varying load conditions. The system demonstrates linear scalability up to 32 nodes, with high availability and failover capabilities. The comprehensive assessment validates the framework's effectiveness in enterprise environments, providing practical solutions for financial institutions facing evolving fraud challenges. This research contributes to the advancement of financial security technology through the innovative application of adversarial learning in fraud detection.

An ultra-lightweight network combining Mamba and frequency-domain feature extraction for pavement tiny-crack segmentation

Pixel-level detection and segmentation of pavement cracks is a crucial but challenging task. Current deep learning methods mainly target larger cracks, often overlooking tiny cracks. Additionally, improvements in these methods typically involve more complex network structures, leading to reduced efficiency and higher computational demands. To tackle these challenges, we propose an Ultra-lightweight Network (ULNet) designed for tiny crack segmentation. ULNet boasts a low parameter count and minimal FLOPs, comprising three main components: the Mamba feature extraction branch, the frequency domain feature extraction branch, and the feature decoder. The Mamba feature extraction branch utilizes the proposed cross-visual Mamba feature extraction module, which enhances the accuracy of tiny crack detection by extracting global features through layer-by-layer processing and cross-fusion of feature information, all without relying on an attention mechanism. The frequency domain feature extraction branch transforms input features into the frequency domain and applies a Gaussian high-pass filter to smooth the image and extract high-frequency crack details while suppressing noise. Finally, the feature decoder incorporates a multi-scale feature selection and fusion module that integrates both channel and spatial attention, enabling effective extraction of tiny crack features from adjacent scales. Inspired by the mechanisms of biological visual information processing, we integrate the high-frequency features from the frequency domain feature extraction branch with the features from the Mamba feature extraction branch. This integration follows the interactive patterns of biological vision, allowing for layer-wise fusion and interaction of features, thereby supplementing the information from the Mamba branch and reducing information loss. We tested and evaluated ULNet on several public datasets (BJN260, Rain365, Sun520, and DeepCrack). Experimental results indicate that our method achieves state-of-the-art performance among all lightweight approaches.

GKA-GPT: Graphical knowledge aggregation for multiturn dialog generation

In human interaction, effective communication relies on shared cognitive processes that facilitate the ability of individuals to comprehend the intended message of their interlocutors. Recent research in multiturn dialog generation seeks to emulate human-like responses by incorporating external knowledge into generative models to enhance language understanding. These models often utilize graphical representations of knowledge and employ graph neural networks (GNNs) to capture dialog semantics. However, sole reliance on external knowledge can fall short as human cognition integrates universal commonsense and personal knowledge, with the latter being derived from individual experiences and frequently disregarded. To remedy this, we propose GKA-GPT, a novel GNN-based approach that merges commonsense and personal knowledge into a comprehensive cognition graph to enhance the relevance and diversity of responses in multiturn dialog scenarios. Furthermore, GKA-GPT introduces a multigrained graphical knowledge aggregation mechanism for effective semantic information processing across various levels. Our experiments demonstrate that GKA-GPT outperforms existing baselines by generating more relevant and informative responses.

Frontal Deactivation and the Efficacy of Statistical Learning: Neural Mechanisms Accompanying Exposure to Visual Statistical Sequences.

Statistical learning is the cognitive ability to rapidly identify structure and meaning in unfamiliar streams of sensory experience, even in the absence of feedback. Despite extensive studies, the neurocognitive mechanisms underlying this phenomenon still require further clarification under varying cognitive conditions. Here, we examined neural mechanisms during the first exposure to visually presented sequences in 47 healthy participants. We used two types of visual objects: abstract symbols and pictures of cartoon-like animals. This allowed us to compare informational processing mechanisms with defined distinguishing features. Participants achieved better performance for sequences with easy-to-name than difficult-to-name abstract stimuli. fMRI results revealed greater activation in widespread brain regions in response to random versus statistical sequences for all stimuli types. Behavioral accuracy was associated with increased deactivation of the ventromedial pFC for easy-to-name statistical versus random sequences. For difficult-to-name statistical versus random sequences, performance correlated with dmPFC deactivation. ROI analysis showed a generally positive involvement of the caudate head in sequence processing with significantly stronger activity during the first run of performing the task. Functional connectivity analysis of prefrontal deactivation regions revealed significant connectivity with nodes of the salience network for both object types and inverse connectivity with the caudate head only for easy-to-name objects. The results indicated that distinct subregions of pFC modulate task performance depending on the visual stimulus characteristic. They also showed that among striatal regions, only the head of the caudate was sensitive to initial exposure to visual statistical information.

Mathematical modeling and investigation of intrinsic interaction mechanisms in CO2 laser processing of PMMA

Mathematical modeling and investigation of intrinsic interaction mechanisms in CO2 laser processing of PMMA

Multivariate Pattern Analysis of EEG Reveals Neural Mechanism of Naturalistic Target Processing in Attentional Blink.

The human brain has inherent limitations in consciously processing visual information. When individuals monitor a rapid sequence of images for detecting two targets, they often miss the second target (T2) if it appears within a short time frame of 200-500ms after the first target (T1), a phenomenon known as the attentional blink (AB). The neural mechanism behind the attentional blink (AB) remains unclear, largely due to the use of simplistic visual items such as letters and digits in conventional AB experiments, which differ significantly from naturalistic vision. This study employs advanced multivariate pattern analysis (MVPA) of human EEG data (including 17 females and 18 males) to explore the neural representations associated with target processing within a naturalistic paradigm under conditions where AB does or does not occur. Our MVPA analysis successfully decoded the identity of target images from EEG data. Moreover, in the AB condition, characterized by a limited time between targets, Tl processing coincided with T2 processing, resulting in the suppression of late representational markers of both Tl and T2. Conversely, in the condition with longer inter-target interval, neural representations endured for a longer duration. These findings suggest that the attentional blink can be attributed to the suppression of neural representations in the later stages of target processing.Significance Statement Within a naturalistic paradigm, we investigated the phenomenon known as attentional blink, where individuals struggle to identify a second target in a rapid sequence when the first target precedes it too closely. Attentional blink is purported to reflect an apparent bottleneck in the attention system's ability to rapidly redirect attentional resources; however, the mechanism underlying this phenomenon remains hotly debated. Our findings reveal that during a rapid presentation of natural images, a short temporal gap between targets results in reduced neural repre-sentations of targets and the occurrence of attentional blink. Conversely, when a greater temporal gap exists between targets, neural representations are preserved. This study provides valuable insights into how the human brain per-ceives the ever-changing visual world around us.

Anxiety symptoms are differentially associated with facial expression processing in boys and girls.

Facial expressions convey important social information and can initiate behavioral change through the processing and understanding of emotions. However, while this ability is known to evolve throughout development, it remains unclear whether this ability differs between girls and boys or how other variables such as level of anxiety can modulate it. Furthermore, understanding the underlying neural mechanisms of facial expression processing and how they are linked by sex and anxiety during development is essential, as alterations in this processing have been associated with psychiatric disorders. Herein, 191 typically-developing youth (6-15 years-old) completed an implicit face processing task involving three facial expressions (angry, happy and neutral) during functional magnetic resonance imaging. We conducted linear models on the fMRI data to investigate the impact sex and anxiety on brain responses to emotional faces, accounting for age. Our findings indicated a significant anxiety-by-sex interaction in a posterior network covering bilateral visual and medial temporal cortices during the happy > neutral contrast. Specifically, girls with higher anxiety showed weaker activation while boys showed the opposite pattern. These findings suggest that the inter-subject variability reported in typically developing individuals in response to facial emotions may be related to many factors, including sex and anxiety level.

Bio-Inspired P-type TeSeOx Synaptic Transistor Based on Multispectral Sensing for Neuromorphic Visual Multilevel Nociceptor.

The development of neuromorphic color vision has significant research implications in the fields of machine vision and artificial intelligence. By mimicking the processing mechanisms of energy-efficient biological visual systems, it offers a unique potential for real-time color environment perception and dynamic adaptability. This paper reports on a multispectral color sensing synaptic device based on a novel p-type TeSeOx transistor, applied to a neuromorphic visual multilevel nociceptor. Due to the intrinsic properties of TeSeOx, its narrow bandgap allows for multi-wavelength (405, 532, 655 nm) response, and its oxide semiconductor-based persistent photoconductivity converts optical signals into stored electrical signals, successfully emulating key synaptic characteristics such as excitatory postsynaptic current (EPSC), multi-pulse facilitation, and the transition from short-term to long-term memory. Additionally, it simulates learning, forgetting, and relearning behaviors, as well as image memory under tricolor light. Finally, using optical signals as a pain stimulus, the fundamental functions of a nociceptor are realized, including "threshold," "non-adaptation," "relaxation," and "nociceptive sensitization". More importantly, by using tricolor light, multilevel pain perception is acheived. These results have the potential to advance fields such as autonomous driving, machine vision, and intelligent alert systems.

Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets

Many software programs can be used to train psychology graduate students and researchers to perform various types of exploratory and confirmatory factor analyses. However, the mechanics of the model fitting process are generally opaque to the student or researcher when using these programs. We propose that spreadsheets can be a very effective tool for enabling psychology students to obtain a better understanding of what is “under the hood” of the factor analysis methods they are implementing with popular software packages such as SPSS, AMOS, R, or MPlus. In this paper we present an Excel workbook that illustrates exploratory factor analysis via the principal axis factoring method and maximum likelihood estimation. Varimax and oblimin rotation of factor loadings is also demonstrated. We also illustrate several different confirmatory factor analysis models: (i) the unidimensional model, (ii) the multiple uncorrelated factors model, (iii) the multiple correlated factors model, and (iv) the bifactor model. Although we are not advocating Excel spreadsheets as the method of choice for factor analysis, we believe they are excellent tools for teaching the mechanics of factor analysis to psychology graduate students and researchers.

Evaluation of Finger Movement Impairment Level Recognition Method Based on Fugl-Meyer Assessment Using Surface EMG

Subjectivity has been an inherent issue in the conventional Fugl-Meyer assessment, which has been the focus of impairment-level recognition in several studies. This study continues our previous work on the use of EMG to recognize finger movement impairment levels. In contrast to our previous work, this study provided a better and more reliable recognition result with improved experimental settings, such as an increased sampling frequency, EMG channels, and extensive patient data. This study employed two data processing mechanisms, inter-subject cross-validation (ISCV) and data-scaled inter-subject cross-validation (DS-ISCV), resulting in two evaluation methods. The machine learning algorithms employed in this study were SVM, random forest (RF), and multi-layer perceptron (MLP). MLP_ISCV achieved the highest average recall score of 0.73 across impairment levels in the spherical grasp task. Subsequently, the highest average recall score of 0.72 among non-majority classes was achieved by SVM_DS-ISCV in the mass extension task. The cross-validation result shows that the proposed method effectively handled the imbalanced dataset without being biased toward the majority class. The proposed method demonstrated the potential to assist doctors in clarifying the subjective assessment of finger movement impairment levels.

Mathematical anxiety is associated with reduced visuospatial working memory: the impact of emotional and spatial distracting information

AbstractThe attentional control theory posits that anxiety may shift attention towards threatening stimuli and/or affect general attentional control mechanisms. The spatial theory suggests that mathematical anxiety (MA) originates from weakness in spatial processing mechanisms such as visuospatial working memory. Accordingly, MA may negatively influence (1) attentional mechanisms in anxious states and/or (2) general attentional control or working memory mechanisms. The current study examined the associations between MA, attention mechanisms, and working memory. To this end, we validated a new set of math-related pictures and used a math-related modified version of a flanker task. Two groups of college students with low (N = 35) and high (N = 31) MA were asked to perform a central task and ignore math-related and neutral distracting pictures. After we controlled for general anxiety, participants with high MA exhibited a larger location congruency effect in the modified flanker task. Moreover, visuospatial short-term and working memory fully mediated the relationship between MA and performances. These results suggest that general visuospatial deficits constitute the main difficulty among individuals with MA. Taken together, the findings shed light on the unique difficulty of individuals with HMA ignoring distraction, and suggest that reduced visuospatial abilities are at the basis of this difficulty. These findings are discussed in the context of their theoretical and treatment-related potential implications.

Splashing effects and mechanism in water jet-guided laser processing of Cf/SiC composites

Water jet-guided laser (WJGL) processing of ceramic matrix composites offers smooth cutting surfaces and minimizes defects such as delamination, burrs, and recast layers. However, the processing ability of WJGL is limited by the stability of the water jet. Splashing is a critical factor that affects water jet stability. This study investigates the splashing morphology and its impact mechanism during WJGL processing of continuous carbon fiber reinforced silicon carbide (Cf/SiC) composites. High-speed cameras were used to capture splashing morphologies and laser transmission states during drilling and grooving. The results indicate that the splashing morphology was significantly affected by the water jet speed and the micro-hole/groove depth, resulting in behaviors such as water accumulation, droplets falling, rebound droplets, splash impact, water film, and mist, which distorted or even broke the water jet. The laser escaped in the distorted water jet, leading to a reduction in material removal rate. The splashing at the water jet speed of 160 m/s resulted in a 61.1 % reduction in material removal rate during drilling compared to 40 m/s. In addition, a method of placing a porous water-absorbing material on the workpiece surface was proposed, which effectively improved the material removal rate. This paper presents a theoretical basis for comprehending and addressing splashing in WJGL processing.

Differential neural representations of syntactic and semantic information across languages in Chinese-English bilinguals

Bilingual individuals manage multiple languages that align in conceptual meaning but differ in forms and structures. While prior research has established foundational insights into the neural mechanisms in bilingual processing, the extent to which the first (L1) and second language (L2) systems overlap or diverge across different linguistic components remains unclear. This study probed the neural underpinnings of syntactic and semantic processing for L1 and L2 in Chinese-English bilinguals (N = 44) who performed sentence comprehension tasks and an N-back working memory task during functional MRI scanning. We observed that the increased activation for L2 processing was within the verbal working memory network, suggesting a greater cognitive demand for processing L2. Crucially, we looked for brain regions showing adaptation to the repetition of semantic information and syntactic structure, and found more robust adaptation effects in L1 in the middle and superior temporal cortical areas. The differential adaptation effects between L1 and L2 were more pronounced for the semantic condition. Multivariate pattern analysis further revealed distinct neural sensitivities to syntactic and semantic representations between L1 and L2 across frontotemporal language regions. Our findings suggest that while L1 and L2 engage similar neural systems, finer representation analyses uncover distinct neural patterns for both semantic and syntactic aspects in the two languages. This study advances our understanding of neural representations involved in different language components in bilingual individuals.

Cross-shard transaction optimization based on community detection in sharding blockchain systems

Blockchain systems have always faced the challenge of performance bottlenecks, and sharding technology is considered a promising mainstream on-chain scalability solution to solve this problem. Due to the complexity and high cost of the cross-shard transaction processing mechanism in the sharding blockchain system, as well as the high proportion of cross-shard transactions, it becomes challenging for the sharding blockchain system to reach the ideal theoretical performance upper limit. Therefore, this paper aims to reduce the proportion of cross-shard transactions by dividing accounts with frequent transactions into the same shard, thereby improving system throughput. This paper builds a hypergraph based on historical transaction data to represent the diverse transaction relationships between accounts, and formulates the account division problem in the blockchain as a community discovery problem on the hypergraph structure. A time-aware community detection algorithm is proposed to partition accounts by considering the sustainability of transaction relationships between accounts. This also solves the problem of community detection algorithms tending to partition into larger shards. In addition, this paper builds a local Ethereum test network and implements the proposed algorithm on a real transaction dataset. Experimental results show that this algorithm can reduce the proportion of cross-shard transactions from about 95% to about 10%. Furthermore, it shows superior performance in terms of transaction throughput and latency compared with other community detection-based account partitioning algorithms.

A Review of Magnetic Abrasive Finishing for the Internal Surfaces of Metal Additive Manufactured Parts

With the rapid development of high-end manufacturing industries such as aerospace and national defense, the demand for metal additive manufactured parts with complex internal cavities has been steadily increasing. However, the finishing of complex internal surfaces, especially for irregularly shaped parts, remains a significant challenge due to their intricate geometries. Through a comparative analysis of common finishing methods, the distinctive characteristics and applicability of magnetic abrasive finishing (MAF) are highlighted. To meet the finishing needs of complex metal additive manufactured parts, this paper reviews the current research on magnetic abrasive finishing devices, processing mechanisms, the development of magnetic abrasives, and the MAF processes for intricate internal cavities. Future development trends in MAF for complex internal cavities in additive manufactured parts are also explored; these are (1) investigating multi-technology composite magnetic abrasive finishing equipment designed for complex internal surfaces; (2) studying the dynamic behavior of multiple magnetic abrasive particles in complex cavities and their material removal mechanisms; (3) developing high-performance magnetic abrasives suitable for demanding conditions; and (4) exploring the MAF process for intricate internal surfaces.