Neuromorphic Integrated Sensing and Communications
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Abstract (arXiv)
Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has introduced the concept of neuromorphic communications, whereby neuromorphic computing is integrated with impulse radio (IR) transmission to implement low-energy and low-latency remote inference in wireless Internet-of-Things (IoT) networks. In this paper, we introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and to detect the radar target using directly the received signal. The SNN operation is optimized by balancing performance metrics for data communications and radar sensing, highlighting synergies and trade-offs between the two applications.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Sep 2022.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.
9 papers.
- 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guaranteescited by 8
- 2024Neuromorphic Wireless Split Computing with Multi-Level Spikescited by 3
- 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunitiescited by 3
- 2023Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computingcited by 2
- 2024Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneckcited by 1
- 2025Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learningcited by 1
- 2025Neuromorphic Wireless Split Computing with Resonate-and-Fire Neuronscited by 0
- 2025Online Reliable Anomaly Detection via Neuromorphic Sensing and Communicationscited by 0
- 2026ITP-STDP: A Hardware-Efficient Intrinsic-Timing Power-of-Two Synaptic Learning Engine for On-Chip SNNscited by 0
- 2026ITP-STDP: A Hardware-Efficient Intrinsic-Timing Power-of-Two Synaptic Learning Engine for On-Chip SNNs4 Jun 2026
- 2025Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications16 Oct 2025
- 2025Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities23 Aug 2025
- 2025Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning26 Jun 2025
- 2025Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons24 Jun 2025
- 2024Neuromorphic Wireless Split Computing with Multi-Level Spikes7 Nov 2024
- 2024Neuromorphic Wireless Device-Edge Co-Inference via the Directed Information Bottleneck2 Apr 2024
- 2023Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing22 Aug 2023
- 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees18 May 2023