# Neuromorphic Computing and Sensing in Space arXiv: https://arxiv.org/abs/2212.05236 ยท HTML: https://mlsanity.com/paper/2212.05236/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 138 references that are in the map. - First posted on arXiv 10 Dec 2022. ## Builds on (most cited first, 27 of 27) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017) - [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019) - [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021) - [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017) - [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018) - [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018) - [Supervised learning based on temporal coding in spiking neural networks](https://mlsanity.com/paper/1606.08165/) (2016) - [Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networks](https://mlsanity.com/paper/2009.08378/) (2020) - [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017) - [Fast and energy-efficient neuromorphic deep learning with first-spike times](https://mlsanity.com/paper/1912.11443/) (2019) - [S4NN: temporal backpropagation for spiking neural networks with one spike per neuron](https://mlsanity.com/paper/1910.09495/) (2019) - [Local plasticity rules can learn deep representations using self-supervised contrastive predictions](https://mlsanity.com/paper/2010.08262/) (2020) - [Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation](https://mlsanity.com/paper/1907.13223/) (2019) - [BindsNET: A machine learning-oriented spiking neural networks library in Python](https://mlsanity.com/paper/1806.01423/) (2018) - [The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity](https://mlsanity.com/paper/2201.11063/) (2022) - [Star Tracking using an Event Camera](https://mlsanity.com/paper/1812.02895/) (2018) - [Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing](https://mlsanity.com/paper/2209.11945/) (2022) - [In-Hardware Learning of Multilayer Spiking Neural Networks on a Neuromorphic Processor](https://mlsanity.com/paper/2105.03649/) (2021) - [A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas](https://mlsanity.com/paper/2005.06318/) (2020) - [Utility and Feasibility of a Center Surround Event Camera](https://mlsanity.com/paper/2202.13076/) (2022) - [Globally Optimal Event-Based Divergence Estimation for Ventral Landing](https://mlsanity.com/paper/2209.13168/) (2022) - [Human peripheral blur is optimal for object recognition](https://mlsanity.com/paper/1807.08476/) (2018) - [Stochastic inference with spiking neurons in the high-conductance state](https://mlsanity.com/paper/1610.07161/) (2016) - [Relational representation learning with spike trains](https://mlsanity.com/paper/2205.09140/) (2022) - [Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging](https://mlsanity.com/paper/2211.09939/) (2022) - [Neuro-symbolic computing with spiking neural networks](https://mlsanity.com/paper/2208.02576/) (2022) ## Built on by (most cited first, 10 of 10) - [On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing](https://mlsanity.com/paper/2308.00394/) (2023) - [Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation](https://mlsanity.com/paper/2409.06240/) (2024) - [Spiking monocular event based 6D pose estimation for space application](https://mlsanity.com/paper/2501.02916/) (2025) - [Synthetic Lunar Terrain: A Multimodal Open Dataset for Training and Evaluating Neuromorphic Vision Algorithms](https://mlsanity.com/paper/2408.16971/) (2024) - [Causal pieces: analysing and improving spiking neural networks piece by piece](https://mlsanity.com/paper/2504.14015/) (2025) - [Event-RGB Fusion for Spacecraft Pose Estimation Under Harsh Lighting](https://mlsanity.com/paper/2507.05698/) (2025) - [Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs](https://mlsanity.com/paper/2509.04506/) (2025) - [Equivalence of approximation by networks of single- and multi-spike neurons](https://mlsanity.com/paper/2603.13478/) (2026) - [Tracking Particles Ejected From Active Asteroid Bennu With Event-Based Vision](https://mlsanity.com/paper/2309.06819/) (2023) - [Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics](https://mlsanity.com/paper/2603.13880/) (2026) ## 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).