MANIN: Multi-Attention Neural Interaction Networks for Social Rumor Detection

With the fast-changing development of emerging online media, it has be-come apparent that information on social networks is characterized by extensive, fast and timely spreading. The absence of effective detection methods and moni-toring means has led to a massive outbreak of rumors. Therefore, accurate detection and timely suppression of rumors in social networks is a vital task in maintaining social security and purifying public networks. Most existing work relies only on monotonous textual content and shallow semantic information, and lacks critical at-tention to and potential mining of user relationships. Such being the case, we can better improve these problems by employing attention mechanisms. In this paper, we proposea Multi-Attention Neural Interaction Network (MANIN) for rumor detection, which consists mainly of a self-attention-based BERT encoder, a post-comment co-attention mechanism, and a graph attention neural network for mining potential user interactions. We have conducted numerous experiments on real datasets and verified their validity, and the results show that the model proposed by us outperforms existing models with an accuracy rate of 81.6%.

Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalographic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

Extracting single-trial neural interaction using latent dynamical systems model

In systems neuroscience, advances in simultaneous recording technology have helped reveal the population dynamics that underlie the complex neural correlates of animal behavior and cognitive processes. To investigate these correlates, neural interactions are typically abstracted from spike trains of pairs of neurons accumulated over the course of many trials. However, the resultant averaged values do not lead to understanding of neural computation in which the responses of populations are highly variable even under identical external conditions. Accordingly, neural interactions within the population also show strong fluctuations. In the present study, we introduce an analysis method reflecting the temporal variation of neural interactions, in which cross-correlograms on rate estimates are applied via a latent dynamical systems model. Using this method, we were able to predict time-varying neural interactions within a single trial. In addition, the pairwise connections estimated in our analysis increased along behavioral epochs among neurons categorized within similar functional groups. Thus, our analysis method revealed that neurons in the same groups communicate more as the population gets involved in the assigned task. We also showed that the characteristics of neural interaction from our model differ from the results of a typical model employing cross-correlation coefficients. This suggests that our model can extract nonoverlapping information about network topology, unlike the typical model.

Bladder urothelium converts bacterial lipopolysaccharide information into neural signaling via an ATP-mediated pathway to enhance the micturition reflex for rapid defense

When bacteria enter the bladder lumen, a first-stage active defensive mechanism flushes them out. Although urinary frequency induced by bacterial cystitis is a well-known defensive response against bacteria, the underlying mechanism remains unclear. In this study, using a mouse model of acute bacterial cystitis, we demonstrate that the bladder urothelium senses luminal extracellular bacterial lipopolysaccharide (LPS) through Toll-like receptor 4 and releases the transmitter ATP. Moreover, analysis of purinergic P2X2 and P2X3 receptor-deficient mice indicated that ATP signaling plays a pivotal role in the LPS-induced activation of L6–S1 spinal neurons through the bladder afferent pathway, resulting in rapid onset of the enhanced micturition reflex. Thus, we revealed a novel defensive mechanism against bacterial infection via an epithelial-neural interaction that induces urinary frequency prior to bacterial clearance by neutrophils of the innate immune system. Our results indicate an important defense role for the bladder urothelium as a chemical-neural transducer, converting bacterial LPS information into neural signaling via an ATP-mediated pathway, with bladder urothelial cells acting as sensory receptor cells.

A Brain Computer Interface for Smart Home Control

A new communication channel called brain computer interface (BCI) which is between the brain of human and a digital computer. Its goal is to restore movements, restoring communication, restoring environmental control for disabled people. The natural communication and control is alternated using this system. The neuromuscular channels which are the efficient pathway of our human body are bypassed by BCI’s artificial system. The varying patterns which are produced due to neural interactions results in the different states of brain. The different patterns of waves having different ranges of frequencies and amplitudes are produced by the patterns of neural interaction which is performed by using multiple neurons. These interactions with the neurons lead to the electrical discharge in smaller ranges. This project deals with brain signals which are sensed by the sensor in the head. These signals are divided into packets of data which are then will be transmitted into a wireless medium such as Bluetooth. The unit which is measuring the brain wave will receive the raw data from the sensor and it is interfaced to microcontroller. The output data from the microcontroller is sent to the operation process in home section such as modules of bulb and fan. Depending on the alpha and theta wave amplitudes, the on, off condition of home appliances is varied. This helps in the easy operation of home electrical appliances for aged people and paralyzed patients. Since smart technologies are becoming very popular in recent times, this kind of application of smart technology in home control finds very useful and helpful.