Neuromorphic computing systems are designed to mimic the structure and function of the human brain and nervous system by using parallel computation to simulate the massive parallelism of biological systems. The result is a highly energy-efficient and high-performance computing, making neuromorphic systems a promising area of research for Artificial Intelligence (AI) and Machine Learning (ML). Sensory-inspired event-driven neuromorphic computing is inspired by the way biological processing of sensory inputs and information. In fact, external stimuli activate sensory neurons, triggering an electric signal propagating through the neural network in an event-driven fashion.
The aim of sensory-inspired neuromorphic computing is to reproduce event-driven processing in artificial systems that can process sensory information in real-time with minimal energy consumption. In fact, only the relevant sensory events trigger neural activity while traditional deep learning approaches rely on batch processing of large datasets and can be computationally expensive. Bridging AI and biological systems, sensory-inspired neuromorphic computing supports the advancement of AI and ML applications in robotics, medical devices, and more fields. Surely, we face several challenges such as algorithms development, scalable hardware architecture able to process the information.
In this research Topics we aim to explore the current state of the art in the field as well as address the challenges we are facing. We welcome contributions exploring, but not limited to, the following themes:
• Event-driven neuromorphic computing
• Algorithms implementation
• Neuromorphic sensors development and impact
• Event-based unsupervised object tracking
• Event-based supervised and/or unsupervised object recognition
Neuromorphic computing systems are designed to mimic the structure and function of the human brain and nervous system by using parallel computation to simulate the massive parallelism of biological systems. The result is a highly energy-efficient and high-performance computing, making neuromorphic systems a promising area of research for Artificial Intelligence (AI) and Machine Learning (ML). Sensory-inspired event-driven neuromorphic computing is inspired by the way biological processing of sensory inputs and information. In fact, external stimuli activate sensory neurons, triggering an electric signal propagating through the neural network in an event-driven fashion.
The aim of sensory-inspired neuromorphic computing is to reproduce event-driven processing in artificial systems that can process sensory information in real-time with minimal energy consumption. In fact, only the relevant sensory events trigger neural activity while traditional deep learning approaches rely on batch processing of large datasets and can be computationally expensive. Bridging AI and biological systems, sensory-inspired neuromorphic computing supports the advancement of AI and ML applications in robotics, medical devices, and more fields. Surely, we face several challenges such as algorithms development, scalable hardware architecture able to process the information.
In this research Topics we aim to explore the current state of the art in the field as well as address the challenges we are facing. We welcome contributions exploring, but not limited to, the following themes:
• Event-driven neuromorphic computing
• Algorithms implementation
• Neuromorphic sensors development and impact
• Event-based unsupervised object tracking
• Event-based supervised and/or unsupervised object recognition