The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

Sound source localization is a critical problem in various fields, including communication, security, and entertainment. Binaural cues are a natural technique used by mammalian ears for efficient sound source localization. Spiking neural networks (SNNs) have emerged as a promising tool for implementing binaural sound source localization approaches. However, optimizing the topology and size of SNNs is crucial to reduce computational costs while maintaining accuracy. This paper proposes a real-time structure of a reservoir SNN (rSNN) called Adaptive-Resonance-Theory-based rSNN (ART-rSNN) for localizing sound sources in the time domain by integrating an energy-based localization method. The dataset used in this work is recorded by two different omnidirectional microphones from a real environment. The dataset includes various sound events such as speech, music, and environmental sounds. The proposed ART-rSNN architecture can dynamically adjust the location of its neurons to amplify estimated energy near the sound source, resulting in higher localization accuracy. Our proposed method outperforms several conventional and state of the art algorithms in terms of accuracy and is able to detect the front and back direction of azimuth angle. This work demonstrates the potential of dynamic neuron arrangements in SNNs for improving sound source localization in practical applications.

Robotic applications across industries demand advanced navigation for safe and smooth movement. Smooth path planning is crucial for mobile robots to ensure stable and efficient navigation, as it minimizes jerky movements and enhances overall performance Achieving this requires smooth collision-free paths. Partial Swarm Optimization (PSO) and Potential Field (PF) are notable path-planning techniques, however, they may struggle to produce smooth paths due to their inherent algorithms, potentially leading to suboptimal robot motion and increased energy consumption. In addition, while PSO efficiently explores solution spaces, it generates long paths and has limited global search. On the contrary, PF methods offer concise paths but struggle with distant targets or obstacles. To address this, we propose Smoothed Partial Swarm Optimization with Improved Potential Field (SPSO-IPF), combining both approaches and it is capable of generating a smooth and safe path. Our research demonstrates SPSO-IPF’s superiority, proving its effectiveness in static and dynamic environments compared to a mere PSO or a mere PF approach.

Autonomous systems, such as self-driving cars, surgical robots, and space rovers, require efficient and collision-free navigation in dynamic environments. Geometric optimal navigation and path planning have become critical research areas, combining geometry, optimization, and machine learning to address these challenges. This paper systematically reviews state-of-the-art methodologies in geometric navigation and path planning, with a focus on integrating advanced geometric principles, optimization techniques, and machine learning algorithms. It examines recent advancements in continuous optimization, real-time adaptability, and learning-based strategies, which enable robots to navigate dynamic environments, avoid moving obstacles, and optimize trajectories under complex constraints. The study identifies several unresolved challenges in the field, including scalability in high-dimensional spaces, real-time computation for dynamic environments, and the integration of perception systems for accurate environment modeling. Additionally, ethical and safety concerns in human–robot interactions are highlighted as critical issues for real-world deployment. The paper provides a comprehensive framework for addressing these challenges, bridging the gap between classical algorithms and modern techniques. By emphasizing recent advancements and unresolved challenges, this work contributes to the broader understanding of geometric optimal navigation and path planning. The insights presented here aim to inspire future research and foster the development of more robust, efficient, and intelligent navigation systems. This survey not only highlights the novelty of integrating geometry, optimization, and machine learning but also provides a roadmap for addressing critical issues in the field, paving the way for the next generation of autonomous systems.

The Epileptor model is a mathematical framework utilized for simulating the transition from interictal to ictal local field potential (LFP) activity in the brain, with the aim of predicting and preventing epileptic seizures. This article introduces a novel approach integrating Lyapunov and Poincaré–Bendixson methods to analyze the stability of limit cycles in nonlinear systems, specifically focusing on Epileptors with a two-state dynamic. Our method accurately delineates the limit cycle boundary through eigenvalue-based analysis, facilitating precise assessment of stability properties and identification of critical regions linked to seizure initiation and termination. Through the investigation of the two-state dynamics of Epileptors, we gain deeper insights into the transition between low activity and seizure states, consequently improving our understanding of epileptic seizures. Our approach can be employed to establish stability conditions and determine the existence of limit cycles in Epileptor models, which can further aid in predicting and preventing epileptic seizures by identifying critical regions associated with seizure initiation and termination. The simulations conducted in this study demonstrate that the model under investigation exhibits stable limit cycle behavior and manifests bifurcation, with significant implications for the development of targeted interventions and more effective prediction and treatments for epilepsy. The findings indicate that the suggested approach establishes that external stimulation should not surpass 10.8 mA. Moreover, the initial normal state lies within the range of −1.6 to −0.1 ictal LFP. On the other hand, the LaSalle and eigenvalue methods individually cannot precisely determine the limit cycle region.

Sound event detection and classification present significant challenges, particularly in noisy environments with multiple overlapping sources. This paper introduces an innovative architecture for multiple sound event detection and classification utilizing recurrent spiking neural networks (SNNs). Our method uniquely leverages temporal data to detect and classify multiple sound sources simultaneously, integrating the physical concept of signal power matching with neuronal output power and employing a binaural strategy to enhance detection accuracy in real-world scenarios. The architecture processes spatiotemporal data to dynamically update synaptic weights, enabling precise identification of sound event categories and their occurrences. Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). Statistical analysis indicates that these improvements are significant (p-value < 0.05). These findings suggest practical applications in various fields such as surveillance, autonomous vehicles, and smart home systems, where robust sound event detection is critical.