Methodology based on spiking neural networks for univariate time-series forecasting

Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains

Brain encodes information through neural spiking activities that modulate external environmental stimuli and underlying internal states. Population of neurons coordinate through functional connectivity to plan movement trajectories and accurately activate neuromuscular activities. Motor Brain-machine interface (BMI) is a platform to study the relationship between behaviors and neural ensemble activities. In BMI, point process filters model directly on spike timings to extract underlying states such as motion intents from observed multi-neuron spike trains. However, these methods assume the encoded information from individual neurons is conditionally independent, which leads to less precise estimation. It is necessary to incorporate functional neural connectivity into a point process filter to improve the state estimation. In this paper, we propose a neural correlation integrated adaptive point process filter (CIPPF) that can incorporate the information from functional neural connectivity from population spike trains in a recursive Bayesian framework. Functional neural connectivity information is approximated by an artificial neural network to provide extra updating information for the posterior estimation. Gaussian approximation is applied on the probability distribution to obtain a closed-form solution. Our proposed method is validated on both simulation and real data collected from the rat two-lever discrimination task. Due to the simultaneous modeling of functional neural connectivity and single neuronal tuning properties, the proposed method shows better decoding performance. This suggests the possibility to improve BMI performance by processing the coordinated neural population activities.

Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation

The bidirectional interactions between brain and heart through autonomic nervous system is the prime focus of neuro-cardiology community. The computer models designed to analyze brain and heart signals are either complex in terms of molecular and cellular interactions or not capable of representing the complex ion channel dynamics. Therefore, scientists are unable to extract the overall behavior of organs by electrical response of heterogeneous cells of brain and heart. In this study, a unified model of excitable cells is proposed that can be modulated by adrenergic features. By implementing the proposed model, a network of one thousand sparsely coupled cardio-neural network is simulated. The major findings of study include i. cardiac heterogeneity in electrical behavior of cardiac myocytes is the prime factor of heart rate variability ii. Brain-heart interplay through electrical pulses holds the necessary information of brain and heart signals that can be analyzed through spiking neural networks iii. Heart rate variability can be predicted and monitored by spiking neural networks from electrophysiological recordings of brain and heart iv. Heart rate variability related to tachycardia and bradycardia depends upon the polarization protocols of cardiac myocytes during plateau phase of action potential. This study provides the modeling and simulation phase of brain-heart interface to predict the morbidity at early stages. The recent advancements in nano-electronics will make is possible to develop brain-heart interface as nano-chip to deploy in subject to stimulate the brain-heart interplay through electrophysiological signals.

Research on Fault Detection by Flow Sequence for Industrial Internet of Things in Sewage Treatment Plant Case

Classifying the flow subsequences of sensor networks is an effective way for fault detection in the Industrial Internet of Things (IIoT). Traditional fault detection algorithms identify exceptions by a single abnormal dataset and do not pay attention to the factors such as electromagnetic interference, network delay, sensor sample delay, and so on. This paper focuses on fault detection by continuous abnormal points. We proposed a fault detection algorithm within the module of sequence state generated by unsupervised learning (SSGBUL) and the module of integrated encoding sequence classification (IESC). Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the different network cards in the IIoT gateway, and then combined the multiple code sequences into one integrated sequence. Next, we classified the integrated sequence by comparing the integrated sequence with the encoding fault type. The results obtained from the three IIoT datasets of a sewage treatment plant show that the accuracy of the SSGBUL-IESC algorithm exceeds 90% with subsequence length 10, which is significantly higher than the accuracies of the dynamic time warping (DTW) algorithm and the time series forest (TSF) algorithm. The proposed algorithm reaches the classification requirements for fault detection for the IIoT.