An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks

Sphingomyelin synthase overexpression increases cholesterol accumulation and decreases cholesterol secretion in liver cells

<p>Abstract</p> <p>Background</p> <p>Studies have shown that plasma high density lipoprotein cholesterol levels are negatively correlated with the development of atherosclerosis, whereas epidemiological studies have also shown that plasma sphingomyelin level is an independent risk factor for atherosclerosis.</p> <p>Methods</p> <p>To evaluate the relationship between cellular sphingomyelin level and cholesterol metabolism, we created two cell lines that overexpressed sphingomyelin synthase 1 or 2 (SMS1 or SMS2), using the Tet-off expression system.</p> <p>Results</p> <p>We found that SMS1 or SMS2 overexpression in Huh7 cells, a human hepatoma cell line, significantly increased the levels of intracellular sphingomyelin, cholesterol, and apolipoprotein A-I and decreased levels of apolipoprotein A-I and cholesterol in the cell culture medium, implying a defect in both processes.</p> <p>Conclusions</p> <p>Our findings indicate that the manipulation of sphingomyelin synthase activity could influence the metabolism of sphingomyelin, cholesterol and apolipoprotein A-I.</p

Adenovirus-mediated sphingomyelin synthase 2 increases atherosclerotic lesions in ApoE KO mice

<p>Abstract</p> <p>Background</p> <p>Sphingomyelin synthase 2 (SMS2) contributes to de novo sphingomyelin (SM) biosynthesis. Its activity is related to SM levels in the plasma and the cell membrane. In this study, we investigated the possibility of a direct relationship between SMS and atherosclerosis.</p> <p>Methods</p> <p>The Adenovirus containing SMS2 gene was given into 10-week ApoE KO C57BL/6J mice by femoral intravenous injection. In the control group, the Adenovirus containing GFP was given. To confirm this model, we took both mRNA level examination (RT-PCR) and protein level examination (SMS activity assay).</p> <p>Result</p> <p>We generated recombinant adenovirus vectors containing either human SMS2 cDNA (AdV-SMS2) or GFP cDNA (AdV-GFP). On day six after intravenous infusion of 2 × 10¹¹particle numbers into ten-week-old apoE KO mice, AdV-SMS2 treatment significantly increased liver SMS2 mRNA levels and SMS activity (by 2.7-fold, 2.3-fold, p < 0.001, respectively), compared to AdV-GFP treated mice. Moreover, plasma total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and sphingomyelin (SM) levels were significantly increased by 39% (p < 0.05), 42% (p < 0.05), 68% (p < 0.001), and 45% (p < 0.05), respectively. Plasma high-density lipoprotein cholesterol (HDL-C), phosphatidylcholine (PC), and PC/SM ratio were decreased by 42% (p < 0.05), 18% (p < 0.05), and 45% (p < 0.05), respectively. On day 30, the atherosclerotic lesions on the aortic arch of AdV-SMS2 treated mice were increased, and the lesion areas on the whole aorta and in the aortic root were significantly increased (p < 0.001). Furthermore, the collagen content in the aorta root was significantly decreased (p < 0.01).</p> <p>Conclusions</p> <p>Our results present direct morphological evidence for the pro-atherogenic capabilities of SMS2. SMS2 could be a potential target for treating atherosclerosis.</p

Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate

Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling temporal dependencies, a trait that has driven their widespread adoption for sequential data processing. Nevertheless, vanilla RNNs are confronted with the well-known issue of gradient vanishing and exploding, posing a significant challenge for learning and establishing long-range dependencies. Additionally, gated RNNs tend to be over-parameterized, resulting in poor network generalization. To address these challenges, we propose a novel Delayed Memory Unit (DMU) in this paper, wherein a delay line structure, coupled with delay gates, is introduced to facilitate temporal interaction and temporal credit assignment, so as to enhance the temporal modeling capabilities of vanilla RNNs. Particularly, the DMU is designed to directly distribute the input information to the optimal time instant in the future, rather than aggregating and redistributing it over time through intricate network dynamics. Our proposed DMU demonstrates superior temporal modeling capabilities across a broad range of sequential modeling tasks, utilizing considerably fewer parameters than other state-of-the-art gated RNN models in applications such as speech recognition, radar gesture recognition, ECG waveform segmentation, and permuted sequential image classification

Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding

The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. To address this problem, this paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model. Specifically, the CE-LIF model incorporates a meticulously designed contextual embedding component into the adaptive neuronal firing threshold, thereby enhancing the memory storage of spiking neurons and facilitating effective sequential modeling. Additionally, theoretical analysis is provided to elucidate how the CE-LIF model enables long-term temporal credit assignment. Remarkably, when compared to state-of-the-art recurrent SNNs, feedforward SNNs comprising the proposed CE-LIF neurons demonstrate superior performance across extensive sequential modeling tasks in terms of classification accuracy, network convergence speed, and memory capacity

LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding

The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biological observation. However, there is a lack of effective solutions for training TTFS-based spiking neural network (SNN). In this paper, we put forward a simple yet effective network conversion algorithm, which is referred to as LC-TTFS, by addressing two main problems that hinder an effective conversion from a high-performance artificial neural network (ANN) to a TTFS-based SNN. We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks, including image classification, image reconstruction, and speech enhancement. With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs. The study, therefore, paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms