About this Research Topic
Neuromorphic Engineering is emerging as a new and groundbreaking discipline at the intersection of neuroscience and electrical-photonic engineering. By merging these fields, it offers innovative and bio-inspired pathways towards achieving unparalleled power efficiency and robustness in technological systems. This interdisciplinary field holds the promise of revolutionizing how we approach computing and sensory technology.
At the core of Neuromorphic Engineering lies the principle of mimicking the neural architectures and processes observed in biological systems. This bio-inspired approach not only enhances the power efficiency and robustness of engineered systems but also provides solutions that are more adaptive and resilient in real-world applications. The integration of neuroscience principles with advanced engineering techniques opens up new avenues for developing cutting-edge technologies.
The most prominent potential outcomes of this interdisciplinary merge include the creation of analogue-based computational primitives. These primitives are particularly suitable for big-data processing, achieving record-level throughput and power consumption. Additionally, Neuromorphic Engineering advances the development of novel bio-inspired sensing modules. These modules effectively combine data-bandwidth sparsity with high accuracy, seamlessly weaving the analogue-digital fabric of our environment.
In this Research Topic, we aim to assemble a comprehensive collection of articles detailing the latest advancements in neuromorphic engineering for computation and sensing, whereas more explicitly focusing on photonic/electro-optic and electronic neuromorphic implementations. We welcome contributions that explore a wide range of topics within this exciting field, including but not limited to:
• Physical reservoir computing
• Spiking neural networks and liquid state machines
• Bio-inspired sensing
• Temporal dynamics for analog computing
• Adaptive control of dynamical systems
• Bio-inspired neural training
• Machine vision-based neuromorphic sensors (such as event-based cameras and haptics)
• Photonic Neuromorphic Hardware
• Integrated Photonic Sensors for Bio-Inspired Sensing Modules
We welcome researchers and experts in these domains to share their groundbreaking work and insights, contributing to the growing body of knowledge in Neuromorphic Engineering.
At the core of Neuromorphic Engineering lies the principle of mimicking the neural architectures and processes observed in biological systems. This bio-inspired approach not only enhances the power efficiency and robustness of engineered systems but also provides solutions that are more adaptive and resilient in real-world applications. The integration of neuroscience principles with advanced engineering techniques opens up new avenues for developing cutting-edge technologies.
The most prominent potential outcomes of this interdisciplinary merge include the creation of analogue-based computational primitives. These primitives are particularly suitable for big-data processing, achieving record-level throughput and power consumption. Additionally, Neuromorphic Engineering advances the development of novel bio-inspired sensing modules. These modules effectively combine data-bandwidth sparsity with high accuracy, seamlessly weaving the analogue-digital fabric of our environment.
In this Research Topic, we aim to assemble a comprehensive collection of articles detailing the latest advancements in neuromorphic engineering for computation and sensing, whereas more explicitly focusing on photonic/electro-optic and electronic neuromorphic implementations. We welcome contributions that explore a wide range of topics within this exciting field, including but not limited to:
• Physical reservoir computing
• Spiking neural networks and liquid state machines
• Bio-inspired sensing
• Temporal dynamics for analog computing
• Adaptive control of dynamical systems
• Bio-inspired neural training
• Machine vision-based neuromorphic sensors (such as event-based cameras and haptics)
• Photonic Neuromorphic Hardware
• Integrated Photonic Sensors for Bio-Inspired Sensing Modules
We welcome researchers and experts in these domains to share their groundbreaking work and insights, contributing to the growing body of knowledge in Neuromorphic Engineering.
Keywords: Adaptive Control of Dynamical Systems, Spiking Neural Networks, Bio-inspired Sensing, Neuromorphic Engineering, Analogue-based Computational Primitives
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
