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Abstract Neuromorphic computing, exemplified by breakthroughs in machine vision through concepts like address-event representation and send-on-delta sampling, has revolutionised sensor technology, enabling low-latency and high dynamic range perception with minimal bandwidth. While these advancements are prominent in vision and auditory perception, their potential in machine olfaction remains under-explored, particularly in the context of fast sensing. Here, we outline the perspectives for neuromorphic principles in machine olfaction. Considering the physical characteristics of turbulent odour environments, we argue that event-driven signal processing is optimally suited to the inherent properties of olfactory signals. We highlight the lack of bandwidth limitation due to turbulent dispersal processes, the characteristic temporal and chemical sparsity, as well as the high information density of the odour landscape. Further, we critically review and discuss the literature on neuromorphic olfaction; particularly focusing on neuromorphic principles such as event generation algorithms, information encoding mechanisms, event processing schemes (spiking neural networks), and learning. We discuss that the application of neuromorphic principles may significantly enhance response time and task performance in robotic olfaction, enabling autonomous systems to perform complex tasks in turbulent environments—such as environmental monitoring, odour guided search and rescue operations, and hazard detection.
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RN‐Net: Reservoir Nodes‐Enabled Neuromorphic Vision Sensing Network
Neuromorphic computing systems promise high energy efficiency and low latency. In particular, when integrated with neuromorphic sensors, they can be used to produce intelligent systems for a broad range of applications. An event‐based camera is such a neuromorphic sensor, inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the event data requires either using expensive feature descriptors to transform spikes into frames, or using spiking neural networks (SNNs) that are expensive to train. In this work, a neural network architecture is proposed, reservoir nodes‐enabled neuromorphic vision sensing network (RN‐Net), based on dynamic temporal encoding by on‐sensor reservoirs and simple deep neural network (DNN) blocks. The reservoir nodes enable efficient temporal processing of asynchronous events by leveraging the native dynamics of the node devices, while the DNN blocks enable spatial feature processing. Combining these blocks in a hierarchical structure, the RN‐Net offers efficient processing for both local and global spatiotemporal features. RN‐Net executes dynamic vision tasks created by event‐based cameras at the highest accuracy reported to date at one order of magnitude smaller network size. The use of simple DNN and standard backpropagation‐based training rules further reduces implementation and training costs.
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- Award ID(s):
- 1900675
- PAR ID:
- 10509229
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Intelligent Systems
- Volume:
- 6
- Issue:
- 12
- ISSN:
- 2640-4567
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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