Left hemisphere predominance of pilocarpine-induced rat epileptiform discharges

<p>Abstract</p> <p>Background</p> <p>The left cerebral hemisphere predominance in human focal epilepsy has been observed in a few studies, however, there is no related systematic study in epileptic animal on hemisphere predominance. The main goal of this paper is to observe if the epileptiform discharges (EDs) of Pilocarpine-induced epileptic rats could present difference between left hemisphere and right hemisphere or not.</p> <p>Methods</p> <p>The electrocorticogram (ECoG) and electrohippocampogram (EHG) from Pilocarpine-induced epileptic rats were recorded and analyzed using Synchronization likelihood (SL) in order to determine the synchronization relation between different brain regions, then visual check and cross-correlation analysis were adopted to evaluate if the EDs were originated more frequently from the left hemisphere than the right hemisphere.</p> <p>Results</p> <p>The data show that the synchronization between left-EHG and right-EHG, left-ECoG and left-EHG, right-ECoG and right-EHG, left-ECoG and right-ECoG, are significantly strengthened after the brain functional state transforms from non-epileptiform discharges to continuous-epileptiform discharges(p < 0.05). When the state transforms from continuous EDs to periodic EDs, the synchronization is significantly weakened between left-ECoG and left-EHG, left-EHG and right-EHG (p < 0.05). Visual check and the time delay (τ) based cross-correlation analysis finds that 10 out of 13 EDs have a left predominance (77%) and 3 out of 13 EDs are right predominance (23%).</p> <p>Conclusion</p> <p>The results suggest that the left hemisphere may be more prone to EDs in the Pilocarpine-induced rat epilepsy model and implicate that the left hemisphere might play an important role in epilepsy states transition.</p

An Enhanced Probabilistic LDA for Multi-Class Brain Computer Interface

There is a growing interest in the study of signal processing and machine learning methods, which may make the brain computer interface (BCI) a new communication channel. A variety of classification methods have been utilized to convert the brain information into control commands. However, most of the methods only produce uncalibrated values and uncertain results.In this study, we presented a probabilistic method "enhanced BLDA" (EBLDA) for multi-class motor imagery BCI, which utilized Bayesian linear discriminant analysis (BLDA) with probabilistic output to improve the classification performance. EBLDA builds a new classifier that enlarges training dataset by adding test samples with high probability. EBLDA is based on the hypothesis that unlabeled samples with high probability provide valuable information to enhance learning process and generate a classifier with refined decision boundaries. To investigate the performance of EBLDA, we first used carefully designed simulated datasets to study how EBLDA works. Then, we adopted a real BCI dataset for further evaluation. The current study shows that: 1) Probabilistic information can improve the performance of BCI for subjects with high kappa coefficient; 2) With supplementary training samples from the test samples of high probability, EBLDA is significantly better than BLDA in classification, especially for small training datasets, in which EBLDA can obtain a refined decision boundary by a shift of BLDA decision boundary with the support of the information from test samples.The proposed EBLDA could potentially reduce training effort. Therefore, it is valuable for us to realize an effective online BCI system, especially for multi-class BCI systems

Regulation of Irregular Neuronal Firing by Autaptic Transmission

The importance of self-feedback autaptic transmission in modulating spike-time irregularity is still poorly understood. By using a biophysical model that incorporates autaptic coupling, we here show that self-innervation of neurons participates in the modulation of irregular neuronal firing, primarily by regulating the occurrence frequency of burst firing. In particular, we find that both excitatory and electrical autapses increase the occurrence of burst firing, thus reducing neuronal firing regularity. In contrast, inhibitory autapses suppress burst firing and therefore tend to improve the regularity of neuronal firing. Importantly, we show that these findings are independent of the firing properties of individual neurons, and as such can be observed for neurons operating in different modes. Our results provide an insightful mechanistic understanding of how different types of autapses shape irregular firing at the single-neuron level, and they highlight the functional importance of autaptic self-innervation in taming and modulating neurodynamics.Comment: 27 pages, 8 figure

A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that simultaneously trains synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher accuracies on various static and neuromorphic datasets than SNNs trained with two single-learning models of the synaptic learning (SL) and the threshold learning (TL). During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework (JDF). Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.Comment: 13 pages, 9 figures, submitted for publicatio

Decoupled Contrastive Multi-view Clustering with High-order Random Walks

In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e., some intra-cluster samples are wrongly treated as negative pairs. Although promising performance has been achieved by these methods, the false negative issue is still far from addressed and the false positive issue emerges because all in- and out-of-neighborhood samples are simply treated as positive and negative, respectively. To address the issues, we propose a novel robust method, dubbed decoupled contrastive multi-view clustering with high-order random walks (DIVIDE). In brief, DIVIDE leverages random walks to progressively identify data pairs in a global instead of local manner. As a result, DIVIDE could identify in-neighborhood negatives and out-of-neighborhood positives. Moreover, DIVIDE embraces a novel MvC architecture to perform inter- and intra-view contrastive learning in different embedding spaces, thus boosting clustering performance and embracing the robustness against missing views. To verify the efficacy of DIVIDE, we carry out extensive experiments on four benchmark datasets comparing with nine state-of-the-art MvC methods in both complete and incomplete MvC settings

Precoding-based blind separation of MIMO FIR mixtures

This paper focuses on the problem of blind separation of sources mixed by multi-input multi-output finite impulse response channels, which is also called convolutive blind source separation (BSS) in short. This problem has been intensively studied in the context that the sources possess certain favourable properties, such as independence and sparsity. However, these properties may not exist in some practical applications. In this paper, we propose a precoding-based convolutive BSS method, which can deal with mutually correlated sources without requiring the sources to be sparse. It is also applicable to mutually independent sources. In the proposed method, the sources are preprocessed in transmitters prior to transmission by order-one precoders. At the receiving side, the second-order statistics of the sources and the Z -domain features of the precoders are exploited to estimate the coded signals, from which the sources are recovered. Simulation results demonstrate the effectiveness of the new convolutive BSS method

Cross-modal Active Complementary Learning with Self-refining Correspondence

Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and textual modalities. However, most existing methods implicitly assume the training pairs are well-aligned while ignoring the ubiquitous annotation noise, a.k.a noisy correspondence (NC), thereby inevitably leading to a performance drop. Although some methods attempt to address such noise, they still face two challenging problems: excessive memorizing/overfitting and unreliable correction for NC, especially under high noise. To address the two problems, we propose a generalized Cross-modal Robust Complementary Learning framework (CRCL), which benefits from a novel Active Complementary Loss (ACL) and an efficient Self-refining Correspondence Correction (SCC) to improve the robustness of existing methods. Specifically, ACL exploits active and complementary learning losses to reduce the risk of providing erroneous supervision, leading to theoretically and experimentally demonstrated robustness against NC. SCC utilizes multiple self-refining processes with momentum correction to enlarge the receptive field for correcting correspondences, thereby alleviating error accumulation and achieving accurate and stable corrections. We carry out extensive experiments on three image-text benchmarks, i.e., Flickr30K, MS-COCO, and CC152K, to verify the superior robustness of our CRCL against synthetic and real-world noisy correspondences.Comment: This paper is accepted by NeurIPS 202

Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition

Human state recognition is a critical topic with pervasive and important applications in human-machine systems.Multi-modal fusion, the combination of metrics from multiple data sources, has been shown as a sound method for improving the recognition performance. However, while promising results have been reported by recent multi-modal-based models, they generally fail to leverage the sophisticated fusion strategies that would model sufficient cross-modal interactions when producing the fusion representation; instead, current methods rely on lengthy and inconsistent data preprocessing and feature crafting. To address this limitation, we propose an end-to-end multi-modal transformer framework for multi-modal human state recognition called Husformer.Specifically, we propose to use cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Using two such attention mechanisms enables effective and adaptive adjustments to noise and interruptions in multi-modal signals during the fusion process and in relation to high-level features. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive workload datasets (MOCAS and CogLoad) demonstrate that in the recognition of human state, our Husformer outperforms both state-of-the-art multi-modal baselines and the use of a single modality by a large margin, especially when dealing with raw multi-modal signals. We also conducted an ablation study to show the benefits of each component in Husformer

A Spatial-channel-temporal-fused Attention for Spiking Neural Networks

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic selection process of salient regions in biological vision systems. Although mechanisms of visual attention have achieved great success in computer vision, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we here propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing historically accumulated spatial-channel information. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and other two SNN models with degenerated attention modules, but also achieves competitive accuracy with existing state-of-the-art methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability to incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that appropriately incorporating cognitive mechanisms of the brain may provide a promising approach to elevate the capability of SNNs.Comment: 12 pages, 8 figures, 5 tabes; This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl