Compared to traditional digital computers, the human nervous system is a parallel, interconnected and massively distributed system, with superior performance and functionality in adaptive learning and data processing. Utilizing artificial machines, brain-inspired computing aims to mimic the architecture and functions of human brain. Atypical hardware and materials beyond conventional von Neumann digital architectures are required for faithful and accurate mimicry of brain-inspired functionalities. Additionally, towards efficient brain-inspired computing systems that are able to perceive, interpret and analyze a real-world, dynamic environment, computing archetypes of biological networks on circuit and hardware level is a first step. As a result, harnessing systems that efficiently connect with biological environments and perceive the world, a new generation of smart sensor application is emerging. Hardware, systems and materials that would be able to interface biology in a dynamic and smart manner is required in this strategy.
Bringing together a diverse and interdisciplinary group of research works on brain-inspired computing along with bio-interfacing and smart sensing, bridging gaps between computing, neuroscience and materials science, and enhancing cross-disciplinary interactions is the goal of this research topic. In this research topic, the focus will be on computations that are time-, event- or data-driven, as done in biological brains.
Topics include but not limited to:
• Time-, event- or data-driven computations in bio-inspired information processing, smart sensors and bioelectronics
• Adaptive bio-interfacing based on time-, event- or data-driven computations
• Bioinspired materials for spike-based neuromorphic systems
• Neural networks and stochastic spiking neurons
Compared to traditional digital computers, the human nervous system is a parallel, interconnected and massively distributed system, with superior performance and functionality in adaptive learning and data processing. Utilizing artificial machines, brain-inspired computing aims to mimic the architecture and functions of human brain. Atypical hardware and materials beyond conventional von Neumann digital architectures are required for faithful and accurate mimicry of brain-inspired functionalities. Additionally, towards efficient brain-inspired computing systems that are able to perceive, interpret and analyze a real-world, dynamic environment, computing archetypes of biological networks on circuit and hardware level is a first step. As a result, harnessing systems that efficiently connect with biological environments and perceive the world, a new generation of smart sensor application is emerging. Hardware, systems and materials that would be able to interface biology in a dynamic and smart manner is required in this strategy.
Bringing together a diverse and interdisciplinary group of research works on brain-inspired computing along with bio-interfacing and smart sensing, bridging gaps between computing, neuroscience and materials science, and enhancing cross-disciplinary interactions is the goal of this research topic. In this research topic, the focus will be on computations that are time-, event- or data-driven, as done in biological brains.
Topics include but not limited to:
• Time-, event- or data-driven computations in bio-inspired information processing, smart sensors and bioelectronics
• Adaptive bio-interfacing based on time-, event- or data-driven computations
• Bioinspired materials for spike-based neuromorphic systems
• Neural networks and stochastic spiking neurons