Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for
numerous real-world activities. Recent deep learning approaches have reached outstanding …

A major characteristic of spiking neural networks (SNNs) over conventional artificial neural
networks (ANNs) is their ability to spike, enabling them to use spike timing for coding and …

Abstract Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-
inspired architecture, and spatio-temporal representation capabilities, have garnered …

Data augmentation is an effective way to overcome the overfitting problem of deep learning
models. However, most existing studies on data augmentation work on framelike data (eg …

Quantum machine learning is in a period of rapid development and discovery, however it
still lacks the resources and diversity of computational models of its classical complement …

Current developments in artificial olfactory systems, also known as electronic nose (e-nose)
systems, have benefited from advanced machine learning techniques that have significantly …

Tactile sensing is a fundamental basis for plenty of robot tasks such as object recognition,
manipulation and gras**. The recently-developed event-driven tactile sensors, which …

This thesis investigates how Spiking Neural Networks, inspired by our brains, can serve as
an alternative to current systems for Visual Place Recognition, a critical component in …