Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification

Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.Comment: 32 pages, 11 Figures, 4 Tables. arXiv admin note: text overlap with arXiv:1511.03198 by other author

Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction

Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current DNN-based supervised online learning models require a large amount of training data and cannot quickly adapt when the underlying system changes. Moreover, these models require continuous retraining with incoming data making them highly inefficient. To solve these issues, we present a novel Continuous Learning-based Unsupervised Recurrent Spiking Neural Network Model (CLURSNN), trained with spike timing dependent plasticity (STDP). CLURSNN makes online predictions by reconstructing the underlying dynamical system using Random Delay Embedding by measuring the membrane potential of neurons in the recurrent layer of the RSNN with the highest betweenness centrality. We also use topological data analysis to propose a novel methodology using the Wasserstein Distance between the persistence homologies of the predicted and observed time series as a loss function. We show that the proposed online time series prediction methodology outperforms state-of-the-art DNN models when predicting an evolving Lorenz63 dynamical system.Comment: Manuscript accepted to be published in IJCNN 202

A study to assess the feasibility of using hemolysis index to predict the corrected potassium in a hemolysed sample

Background: Potassium is one of the most commonly affected analytes in a hemolysed sample. Many formulae have been devised to predict the actual potassium in a hemolysed sample. This study was performed to compare the predicted potassium value in a hemolysed sample to that of potassium value in a non-hemolysed sample of the same patient.Methods: The hemolytic index (HI) derived equation from the paper by Dimeski et al was used to calculate potassium value in this study. A total of 99 paired samples were evaluated where the first sample in a pair was the hemolysed one and the other sample was a non-hemolysed one.Results: This study found that the potassium value in a sample and its respective HI have weak positive correlation. However, there was a statistically significant strong positive correlation between the estimated potassium of hemolysed sample to that of the potassium in the non-hemolysed sample.Conclusions: Hence, we conclude that it is feasible to use HI-derived equation to predict potassium in a hemolysed sample to avoid repetition of each sample

XMD: An Expansive Hardware-telemetry based Mobile Malware Detector to enhance Endpoint Detection

Hardware-based Malware Detectors (HMDs) have shown promise in detecting malicious workloads. However, the current HMDs focus solely on the CPU core of a System-on-Chip (SoC) and, therefore, do not exploit the full potential of the hardware telemetry. In this paper, we propose XMD, an HMD that uses an expansive set of telemetry channels extracted from the different subsystems of SoC. XMD exploits the thread-level profiling power of the CPU-core telemetry, and the global profiling power of non-core telemetry channels, to achieve significantly better detection performance than currently used Hardware Performance Counter (HPC) based detectors. We leverage the concept of manifold hypothesis to analytically prove that adding non-core telemetry channels improves the separability of the benign and malware classes, resulting in performance gains. We train and evaluate XMD using hardware telemetries collected from 723 benign applications and 1033 malware samples on a commodity Android Operating System (OS)-based mobile device. XMD improves over currently used HPC-based detectors by 32.91% for the in-distribution test data. XMD achieves the best detection performance of 86.54% with a false positive rate of 2.9%, compared to the detection rate of 80%, offered by the best performing signature-based Anti-Virus(AV) on VirusTotal, on the same set of malware samples.Comment: Revised version based on peer review feedback. Manuscript to appear in IEEE Transactions on Information Forensics and Securit

EVALUATION OF SERUM TROPONIN I LEVEL IN SEVERE ACUTE RESPIRATORY SYNDROME CORONA VIRUS 2 (SARS COV2) INFECTED PATIENTS ADMITTED IN CORONA VIRUS (COVID) WARD AND INTENSIVE CARE UNIT IN SILCHAR MEDICAL COLLEGE AND HOSPITAL, ASSAM: A RETROSPECTIVE STUDY.

Background:  The pandemic of COVID-19 led to the mortality of a large number of people worldwide. In several studies carried out in different parts of the world, it was seen that cardiac troponin I is a prognosticating biochemical marker of SARS-CoV2-infected patients. This present study aimed to evaluate the serum troponin I level in severe acute respiratory syndrome coronavirus 2 infected patients admitted to covid ward and COVID ICU and to find out any relationship between cardiac Troponin I and disease prognosis. This will aid in early diagnosis.  Methodology: 102 patients participated in this study. Among the 102 patients of SARS COV 2 infection, 49 patients were taken from the Covid ward suffering from mild or moderate form of the disease. The remaining 53 patients were taken from the ICU who were critically ill. Serum cardiac Troponin I value was collected from the Laboratory Information System of the hospital and all the data was analyzed statistically.  Results:  Cardiac Troponin I level is higher in covid positive critically ill patients admitted to ICU with COVID-19. The median (IQR) value of serum cardiac Troponin I is significantly higher (0.0190 ng/ml) in COVID-19 ICU patients than in the COVID patients of the General ward (0.0120 ng/ml). The difference was found to be significant with a p-value of 0.00. A p-value of <0.05 was considered statistically significant.  Conclusion:  Serum Troponin I can be used as a prognosticating marker in COVID-19 infection and a marker of ICU admission in COVID-19-positive patients.  Recommendation:  More studies will be required with a large number of study samples to establish the findings of the present study

Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN

Recurrent Spiking Neural Networks (RSNNs) have emerged as a computationally efficient and brain-inspired learning model. The design of sparse RSNNs with fewer neurons and synapses helps reduce the computational complexity of RSNNs. Traditionally, sparse SNNs are obtained by first training a dense and complex SNN for a target task, and, then, pruning neurons with low activity (activity-based pruning) while maintaining task performance. In contrast, this paper presents a task-agnostic methodology for designing sparse RSNNs by pruning a large randomly initialized model. We introduce a novel Lyapunov Noise Pruning (LNP) algorithm that uses graph sparsification methods and utilizes Lyapunov exponents to design a stable sparse RSNN from a randomly initialized RSNN. We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN). Further, we show that the same sparse HRSNN model can be trained for different tasks, such as image classification and temporal prediction. We experimentally show that, in spite of being task-agnostic, LNP increases computational efficiency (fewer neurons and synapses) and prediction performance of RSNNs compared to traditional activity-based pruning of trained dense models.Comment: Published as a conference paper at ICLR 202

Novel Insights into the Antimicrobial Resistance and Strategies to Curb the Menace

Antibiotics are an essential part of modern healthcare, revolutionizing medicine and saving countless lives worldwide. However, the emergence of antimicrobial resistance (AMR) is a growing concern, with the potential to cause a public health crisis in the future. The aim of this review article is to provide an overview of the microbial and anthropogenic factors contributing to AMR, as well as the consequences of inaction to address the AMR crisis. We searched various international databases such as PubMed, Scopus, ScienceDirect and Google Scholar using “Antimicrobial Resistance”,” Superbug”, “Antibiotic Stewardship”, “One Health’ and “Surveillance” as search keywords in different combinations. We have thoroughly discussed the causes of AMR, such as the overuse and misuse of antibiotics, and the development of resistant strains of bacteria. We have also suggested possible interventions to combat AMR, such as the one health approach, antibiotic stewardship protocols, and the application of artificial intelligence in drug design. Additionally, we have explored the benefits of traditional ethnic medicinal practices in therapy. In conclusion, this review article emphasized the urgent need for a comprehensive and strategic plan to address the issue of AMR. Further in-depth research and novel approaches can mitigate the growing menace of AMR and safeguard both human and animal populations

Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks

Spiking Neural Networks (SNNs) have become an essential paradigm in neuroscience and artificial intelligence, providing brain-inspired computation. Recent advances in literature have studied the network representations of deep neural networks. However, there has been little work that studies representations learned by SNNs, especially using unsupervised local learning methods like spike-timing dependent plasticity (STDP). Recent work by \cite{barannikov2021representation} has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD). Though useful, this method is engineered particularly for feedforward deep neural networks and cannot be used for recurrent networks like Recurrent SNNs (RSNNs). This paper introduces a novel methodology to use RTD to measure the difference between distributed representations of RSNN models with different learning methods. We propose a novel reformulation of RSNNs using feedforward autoencoder networks with skip connections to help us compute the RTD for recurrent networks. Thus, we investigate the learning capabilities of RSNN trained using STDP and the role of heterogeneity in the synaptic dynamics in learning such representations. We demonstrate that heterogeneous STDP in RSNNs yield distinct representations than their homogeneous and surrogate gradient-based supervised learning counterparts. Our results provide insights into the potential of heterogeneous SNN models, aiding the development of more efficient and biologically plausible hybrid artificial intelligence systems.Comment: Accepted in IEEE World Congress on Computational Intelligence (IEEE WCCI) 202

Data_Sheet_1_Heterogeneous recurrent spiking neural network for spatio-temporal classification.pdf

Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence. Although recent SNNs trained with supervised backpropagation show classification accuracy comparable to deep networks, the performance of unsupervised learning-based SNNs remains much lower. This paper presents a heterogeneous recurrent spiking neural network (HRSNN) with unsupervised learning for spatio-temporal classification of video activity recognition tasks on RGB (KTH, UCF11, UCF101) and event-based datasets (DVS128 Gesture). We observed an accuracy of 94.32% for the KTH dataset, 79.58% and 77.53% for the UCF11 and UCF101 datasets, respectively, and an accuracy of 96.54% on the event-based DVS Gesture dataset using the novel unsupervised HRSNN model. The key novelty of the HRSNN is that the recurrent layer in HRSNN consists of heterogeneous neurons with varying firing/relaxation dynamics, and they are trained via heterogeneous spike-time-dependent-plasticity (STDP) with varying learning dynamics for each synapse. We show that this novel combination of heterogeneity in architecture and learning method outperforms current homogeneous spiking neural networks. We further show that HRSNN can achieve similar performance to state-of-the-art backpropagation trained supervised SNN, but with less computation (fewer neurons and sparse connection) and less training data.</p