mlsanityData to 7 Oct 2026

Neuromorphic Computing and Sensing in Space

arXiv 2212.05236 ↗First posted 10 Dec 2022cs.NE cs.AIcited by 10

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Abstract (arXiv)

The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks. Typical examples are novel computer chips designed to mimic the architecture of a biological brain, or sensors that get inspiration from, e.g., the visual or olfactory systems in insects and mammals to acquire information about the environment. This approach is not without ambition as it promises to enable engineered devices able to reproduce the level of performance observed in biological organisms -- the main immediate advantage being the efficient use of scarce resources, which translates into low power requirements. The emphasis on low power and energy efficiency of neuromorphic devices is a perfect match for space applications. Spacecraft -- especially miniaturized ones -- have strict energy constraints as they need to operate in an environment which is scarce with resources and extremely hostile. In this work we present an overview of early attempts made to study a neuromorphic approach in a space context at the European Space Agency's (ESA) Advanced Concepts Team (ACT).

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 27 of its 138 references that are in the map. First posted on arXiv 10 Dec 2022.

Builds on

27 of 138 references.

References · 27

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  3. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  4. 2021Training Spiking Neural Networks Using Lessons From Deep Learningcited by 251
  5. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  6. 2017SuperSpike: Supervised learning in multi-layer spiking neural networkscited by 178
  7. 2018A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimationcited by 178
  8. 2016Supervised learning based on temporal coding in spiking neural networkscited by 75
  9. 2020Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networkscited by 59
  10. 2019Fast and energy-efficient neuromorphic deep learning with first-spike timescited by 52
  11. 2017A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)cited by 51
  12. 2019S4NN: temporal backpropagation for spiking neural networks with one spike per neuroncited by 47
15 more
  1. 2020Local plasticity rules can learn deep representations using self-supervised contrastive predictionscited by 47
  2. 2019Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagationcited by 43
  3. 2018BindsNET: A machine learning-oriented spiking neural networks library in Pythoncited by 42
  4. 2022The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticitycited by 41
  5. 2018Star Tracking using an Event Cameracited by 30
  6. 2022Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensingcited by 21
  7. 2021In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processorcited by 11
  8. 2020A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinascited by 9
  9. 2022Utility and Feasibility of a Center Surround Event Cameracited by 9
  10. 2022Globally Optimal Event-Based Divergence Estimation for Ventral Landingcited by 9
  11. 2018Human peripheral blur is optimal for object recognitioncited by 8
  12. 2016Stochastic inference with spiking neurons in the high-conductance statecited by 6
  13. 2022Relational representation learning with spike trainscited by 4
  14. 2022Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imagingcited by 4
  15. 2022Neuro-symbolic computing with spiking neural networkscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 2.

10 papers.

  1. 2023On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landingcited by 4
  2. 2024Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimationcited by 3
  3. 2025Spiking monocular event based 6D pose estimation for space applicationcited by 3
  4. 2024Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithmscited by 2
  5. 2025Causal pieces: analysing and improving spiking neural networks piece by piececited by 2
  6. 2025Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lightingcited by 1
  7. 2025Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENscited by 1
  8. 2026Equivalence of approximation by networks of single- and multi-spike neuronscited by 1
  9. 2023Tracking Particles Ejected From Active Asteroid Bennu With Event-Based Visioncited by 0
  10. 2026Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Roboticscited by 0