Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to harmonize with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introduces a spiking patch encoding layer to reinforce local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that effectively blends global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model capacity, and simulation steps. An expanded version of our network challenges the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings accentuate the potential of our deep SNN architecture in seamlessly integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.Comment: 11 pages, 6 figure

Unveiling Gene Interactions in Alzheimer\u27s Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach

Alzheimer’s Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD’s pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p \u3c 5 × 10−12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p \u3c 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies

Enhancing Biological and Biomechanical Fixation of Osteochondral Scaffold: A Grand Challenge

Osteoarthritis (OA) is a degenerative joint disease, typified by degradation of cartilage and changes in the subchondral bone, resulting in pain, stiffness and reduced mobility. Current surgical treatments often fail to regenerate hyaline cartilage and result in the formation of fibrocartilage. Tissue engineering approaches have emerged for the repair of cartilage defects and damages to the subchondral bones in the early stage of OA and have shown potential in restoring the joint's function. In this approach, the use of three-dimensional scaffolds (with or without cells) provides support for tissue growth. Commercially available osteochondral (OC) scaffolds have been studied in OA patients for repair and regeneration of OC defects. However, some controversial results are often reported from both clinical trials and animal studies. The objective of this chapter is to report the scaffolds clinical requirements and performance of the currently available OC scaffolds that have been investigated both in animal studies and in clinical trials. The findings have demonstrated the importance of biological and biomechanical fixation of the OC scaffolds in achieving good cartilage fill and improved hyaline cartilage formation. It is concluded that improving cartilage fill, enhancing its integration with host tissues and achieving a strong and stable subchondral bone support for overlying cartilage are still grand challenges for the early treatment of OA

Unveiling Gene Interactions in Alzheimer’s Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach

Alzheimer’s Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD’s pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p −12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies

Reinforcement learning of competitive and cooperative skills in soccer agents

The main aim of this paper is to provide a comprehensive numerical analysis on the efficiency of various reinforcementlearning (RL) techniques in an agent-based soccer game. The SoccerBots is employed as a simulation testbed to analyze the effectiveness of RL techniques under various scenarios. A hybrid agent teaming framework for investigating agent team architecture, learning abilities, and other specific behaviours is presented. Novel RL algorithms to verify the competitiveandcooperativelearning abilities of goal-oriented agents for decision-making are developed. In particular, the tile coding (TC) technique, a function approximation approach, is used to prevent the state space from growing exponentially, hence avoiding the curse of dimensionality. The underlying mechanism of eligibility traces is evaluated in terms of on-policy and off-policy procedures, as well as accumulating traces and replacing traces. The results obtained are analyzed, and implications of the results towards agent teaming and learning are discussed