arXiv subjects

Jens Egholm Pedersen

Publications and source records attributed to Jens Egholm Pedersen.

5 recordsLinked to original sources

arXiv2026

Encoding and Decoding Temporal Signals with Spiking Bandpass Wavelets

Spike-based encodings are sparse and energy-efficient. However, previous theory for such spiking representations is largely disconnected from most signal processing literature and does not extend to multi-scale frames. We recast spike-encoders as time-causal overcomplete wavelet frames with closed-form bandwidth tiling and reconstruction error bounds. We show that scale covariance, frame bounds, and reconstruction guarantees hold for any finite-energy input in both encoding and decoding steps. The proposed spiking bandpass wavelets preserve the sparsity and locality of spiking representations: A threshold parameter sets the event rate, and the reconstruction error grows linearly with it, up to spike quantization and time discretization. On ECG and audio datasets, we reach reconstruction accuracy comparable to continuous wavelet transforms, multi-channel integrate-and-fire time encoding machines, and Sigma-Delta modulation. The experiments demonstrate robustness of the proposed spiking bandpass wavelets to noise and spike-time jitter, and that the spike activity shifts across channels as predicted by the covariance property under temporal rescaling. Every operation reduces to a leaky integrate-and-fire difference with thresholding, and both the encoder and the decoder map directly to existing neuromorphic hardware.

cs.NE↗

arXiv2026

GERD: Geometric event response data generation

Event-based vision sensors offer high temporal resolution, high dynamic range, and low power consumption, yet event-based vision models lag behind conventional frame-based vision methods. We argue that this gap is partly due to the lack of principled study of the transformation groups that govern event-based visual streams. Motivated by the role that geometric and group-theoretic methods have played in advancing computer vision, we present GERD: a simulator for generating event-based recordings of objects under precisely controlled affine, Galilean, and temporal scaling transformations. By providing ground-truth transformations at each timestep, GERD enables hypothesis-driven and controlled studies of geometric properties that are otherwise hard to isolate in real-world datasets or with current event simulators. GERD supports three noise models and sub-pixel motion as a complement to real sensor datasets. We demonstrate its use in training by evaluating models from the literature with geometric guarantees and release GERD as an open tool available at

cs.CV↗

arXiv2026

Scale-covariant spiking wavelets

We establish a theoretical connection between wavelet transforms and spiking neural networks through scale-space theory. We rely on the scale-covariant guarantees in the leaky integrate-and-fire neurons to implement discrete mother wavelets that approximate continuous wavelets. A reconstruction experiment demonstrates the feasibility of the approach and warrants further analysis to mitigate current approximation errors. Our work suggests a novel spiking signal representation that could enable more energy-efficient signal processing algorithms.

cs.NE↗

arXiv2025

Covariant spatio-temporal receptive fields for spiking neural networks

Biological nervous systems constitute important sources of inspiration towards computers that are faster, cheaper, and more energy efficient. Neuromorphic disciplines view the brain as a coevolved system, simultaneously optimizing the hardware and the algorithms running on it. There are clear efficiency gains when bringing the computations into a physical substrate, but we presently lack theories to guide efficient implementations. Here, we present a principled computational model for neuromorphic systems in terms of spatio-temporal receptive fields, based on affine Gaussian kernels over space and leaky-integrator and leaky integrate-and-fire models over time. Our theory is provably covariant to spatial affine and temporal scaling transformations, and with close similarities to the visual processing in mammalian brains. We use these spatio-temporal receptive fields as a prior in an event-based vision task, and show that this improves the training of spiking networks, which otherwise is known as problematic for event-based vision. This work combines efforts within scale-space theory and computational neuroscience to identify theoretically well-founded ways to process spatio-temporal signals in neuromorphic systems. Our contributions are immediately relevant for signal processing and event-based vision, and can be extended to other processing tasks over space and time, such as memory and control.

cs.NE↗

arXiv2022

AEStream: Accelerated event-based processing with coroutines

Neuromorphic sensors imitate the sparse and event-based communication seen in biological sensory organs and brains. Today's sensors can emit many millions of asynchronous events per second, which is challenging to process on conventional computers. To avoid bottleneck effects, there is a need to apply and improve concurrent and parallel processing of events. We present AEStream: a library to efficiently stream asynchronous events from inputs to outputs on conventional computers. AEStream leverages cooperative multitasking primitives known as coroutines to concurrently process individual events, which dramatically simplifies the integration with event-based peripherals, such as event-based cameras and (neuromorphic) asynchronous hardware. We explore the effects of coroutines in concurrent settings by benchmarking them against conventional threading mechanisms, and find that AEStream provides at least twice the throughput. We then apply AEStream in a real-time edge detection task on a GPU and demonstrate 1.3 times faster processing with 5 times fewer memory operations.

cs.DC↗