Neuromorphic Hardware and Computing

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Inspired by human brain, neuromorphic computing technologies have made important breakthroughs in recent years as alternatives to overcome the power and latency shortfalls of traditional digital computing. An interdisciplinary approach is being taken to address the challenge of creating more efficient and intelligent computing systems that can perform diverse tasks, to design hardware with increasing complexity from single device to system architecture level, and to develop new theories and brain-inspired algorithms for future computing.

In this collection at Nature Communications, we aim to bring together cutting-edge research of neuromorphic architectures and hardware, computing and algorithms, applications and invite commentaries from experts. Topics of interest include but are not limited to the following:

  • Neuromorphic Hardware, Compute-in-Memory and Next-Generation Architectures
  • Brain-Inspired Computing and Algorithms, Software Frameworks
  • Spiking Neural Networks, Spike-Timing-Dependent Plasticity, Memory in AI
  • Bio-Inspired Sensing, Event-Driven Sensing, Neuromorphic Sensors
  • Edge and High-Performance Computing, Bio-Signal Processing and Brain-Computer Interface

We welcome the submissions of primary research that fall into any of the above-mentioned categories. All the submissions will be subject to the same peer review process and editorial standard as regular Nature Communications articles.


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Inspired by the ability of mammalian brains for spatial perception, Jiang et al. develop a flexible electronic nerve capable of processing multisensory information for enhanced motion recognition tasks.


Neuromorphic Computing Applications

Brain-inspired Computing and Algorithms

Neuromorphic Architectures and Hardware