No abstract

Temporal lobe epilepsy (TLE) is a chronic neurological disorder that is often resistant to antiepileptic drugs. The pathogenesis of TLE is extremely complicated and remains elusive. Understanding the molecular mechanisms underlying TLE is crucial for its diagnosis and treatment. In the present study, a lithium-pilocarpine-induced TLE model was employed to reveal the pathological changes of hippocampus in rats. Hippocampal samples were taken for proteomic analysis at 2 weeks after the onset of spontaneous seizure (a chronic stage of epileptogenesis). Isobaric tag for relative and absolute quantization (iTRAQ) coupled with liquid chromatography-tandem mass spectrometry (LC–MS/MS) technique was applied for proteomic analysis of hippocampus. A total of 4173 proteins were identified from the hippocampi of epileptic rats and its control, of which 27 differentially expressed proteins (DEPs) were obtained with a fold   change > 1.5 and P < 0.05 . Bioinformatics analysis indicated 27 DEPs were mainly enriched in “regulation of synaptic plasticity and structure” and “calmodulin-dependent protein kinase activity,” which implicate synaptic remodeling may play a vital role in the pathogenesis of TLE. Consequently, the synaptic plasticity-related proteins and synaptic structure were investigated to verify it. It has been demonstrated that CaMKII-α, CaMKII-β, and GFAP were significant upregulated coincidently with proteomic analysis in the hippocampus of TLE rats. Moreover, the increased dendritic spines and hippocampal sclerosis further proved that synaptic plasticity involves in the development of TLE. The present study may help to understand the molecular mechanisms underlying epileptogenesis and provide a basis for further studies on synaptic plasticity in TLE.

Spiking neural networks (SNNs) have recently demonstrated outstanding performance in a variety of high-level tasks, such as image classification. However, advancements in the field of low-level assignments, such as image reconstruction, are rare. This may be due to the lack of promising image encoding techniques and corresponding neuromorphic devices designed specifically for SNN-based low-level vision problems. This paper begins by proposing a simple yet effective undistorted weighted-encoding-decoding technique, which primarily consists of an Undistorted Weighted-Encoding (UWE) and an Undistorted Weighted-Decoding (UWD). The former aims to convert a gray image into spike sequences for effective SNN learning, while the latter converts spike sequences back into images. Then, we design a new SNN training strategy, known as Independent-Temporal Backpropagation (ITBP) to avoid complex loss propagation in spatial and temporal dimensions, and experiments show that ITBP is superior to Spatio-Temporal Backpropagation (STBP). Finally, a so-called Virtual Temporal SNN (VTSNN) is formulated by incorporating the above-mentioned approaches into U-net network architecture, fully utilizing the potent multiscale representation capability. Experimental results on several commonly used datasets such as MNIST, F-MNIST, and CIFAR10 demonstrate that the proposed method produces competitive noise-removal performance extremely which is superior to the existing work. Compared to ANN with the same architecture, VTSNN has a greater chance of achieving superiority while consuming ~1/274 of the energy. Specifically, using the given encoding-decoding strategy, a simple neuromorphic circuit could be easily constructed to maximize this low-carbon strategy.