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cs.NE updates on arXiv.org

MPCS: Neuroplastic Continual Learning via Multi-Component Plasticity and Topology-Aware EWC Combining Trained Models in Reinforcement Learning Training Non-Differentiable Networks via Optimal Transport ShiftLIF: Efficient Multi-Level Spiking Neurons with Power-of-Two Quantization Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance Benchmarking local Hebbian learning rules for memory storage and prototype extraction Robust volatility updates for Hierarchical Gaussian Filtering Spiking Sequence Machines and Transformers Affinity Is Not Enough: Recovering the Free Energy Principle in Mixture-of-Experts Scalable Learning in Structured Recurrent Spiking Neural Networks without Backpropagation Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks Attractor FCM Physical Foundation Models: Fixed hardware implementations of large-scale neural networks When Does Structure Matter in Continual Learning? Dimensionality Controls When Modularity Shapes Representational Geometry Learning to Forget: Continual Learning with Adaptive Weight Decay Causal Learning with Neural Assemblies NORACL: Neurogenesis for Oracle-free Resource-Adaptive Continual Learning Text-Utilization for Encoder-dominated Speech Recognition Models EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming Analysis and Explainability of LLMs Via Evolutionary Methods Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution Primitive Recursion without Composition: Dynamical Characterizations, from Neural Networks to Polynomial ODEs MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks Learn&Drop: Fast Learning of CNNs based on Layer Dropping Architecture-Induced Recoverability Bias in Differentiable Symbolic Regression Collocation-based Robust Physics Informed Neural Networks for time-dependent simulations of pollution propagation under thermal inversion conditions on Spitsbergen Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction
Neuromorphic Cameras in Astronomy: Unveiling the Future o...
Satyapreet Singh Yadav, Bikram Pradhan, Kenil Rajendrabhai Ajudi · 2025-03-20 · via cs.NE updates on arXiv.org

To deepen our understanding of optical astronomy, we must advance imaging technology to overcome conventional frame-based cameras' limited dynamic range and temporal resolution. Our Perspective paper examines how neuromorphic cameras can effectively address these challenges. Drawing inspiration from the human retina, neuromorphic cameras excel in speed and high dynamic range by utilizing asynchronous pixel operation and logarithmic photocurrent conversion, making them highly effective for celestial imaging. We use 1300 mm terrestrial telescope to demonstrate the neuromorphic camera's ability to simultaneously capture faint and bright celestial sources while preventing saturation effects. We illustrate its photometric capabilities through aperture photometry of a star field with faint stars. Detection of the faint gas cloud structure of the Trapezium cluster during a full moon night highlights the camera's high dynamic range, effectively mitigating static glare from lunar illumination. Our investigations also include detecting meteorite passing near the Moon and Earth, as well as imaging satellites and anthropogenic debris with exceptionally high temporal resolution using a 200mm telescope. Our observations show the immense potential of neuromorphic cameras in advancing astronomical optical imaging and pushing the boundaries of observational astronomy.