Artificial Neural Networks (ANNs) are conceptual models of biological neurons that were originally developed to tackle complex problems such as classification, pattern recognition, and forecasting through optimization. However, their high computational demands have raised concerns about the significant energy consumption associated with their use, particularly in large-scale problems and resource-limited applications.
Neuromorphic Engineering (NE) and Computing (NC) offer potential solutions to these issues. Neuromorphic research is an interdisciplinary area that has attracted researchers from diverse backgrounds such as neuroscience, physics, computer science, electrical engineering, and computer engineering. The objective of NE is to replicate the behavior of biological neural networks in circuits and systems, while NC focuses more on application development. In recent years, digital, analog, and mixed-signal circuits and systems have implemented a variety of bio-inspired neuron and network models to replicate brain functionality. Despite this progress, the full potential of neuromorphic computing remains to be realized.
The aim of this Research Topic is to explore the potential applications of neuromorphic computing in different domains. Topics of interest include but are not limited to:
• Designing new computing devices or processors based on neuromorphic computing principles
• Neuromorphic hardware for vision and audio processing applications
• Applications of neuromorphic computing in biomedical engineering
• Novel neuromorphic architectures for specific applications
• Neuromorphic hardware for emerging applications
• Comparative studies of neuromorphic computing and traditional computing for specific applications
Artificial Neural Networks (ANNs) are conceptual models of biological neurons that were originally developed to tackle complex problems such as classification, pattern recognition, and forecasting through optimization. However, their high computational demands have raised concerns about the significant energy consumption associated with their use, particularly in large-scale problems and resource-limited applications.
Neuromorphic Engineering (NE) and Computing (NC) offer potential solutions to these issues. Neuromorphic research is an interdisciplinary area that has attracted researchers from diverse backgrounds such as neuroscience, physics, computer science, electrical engineering, and computer engineering. The objective of NE is to replicate the behavior of biological neural networks in circuits and systems, while NC focuses more on application development. In recent years, digital, analog, and mixed-signal circuits and systems have implemented a variety of bio-inspired neuron and network models to replicate brain functionality. Despite this progress, the full potential of neuromorphic computing remains to be realized.
The aim of this Research Topic is to explore the potential applications of neuromorphic computing in different domains. Topics of interest include but are not limited to:
• Designing new computing devices or processors based on neuromorphic computing principles
• Neuromorphic hardware for vision and audio processing applications
• Applications of neuromorphic computing in biomedical engineering
• Novel neuromorphic architectures for specific applications
• Neuromorphic hardware for emerging applications
• Comparative studies of neuromorphic computing and traditional computing for specific applications
