Spiking Neurons Tune In: AI for Cleaner Radio Signals

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Author: Denis Avetisyan

Researchers have developed an end-to-end artificial intelligence pipeline leveraging spiking neural networks to effectively filter out unwanted radio interference and enhance the clarity of astronomical data.

A unified spiking neural network, trained as a large model and subsequently fragmented, facilitates radio frequency interference detection across diverse neuromorphic platforms-including both snnTorch simulations and SynSense Xylo hardware-with latency encoding of spectrograms and deployment in a standardized NIR format enabling comparative power measurements.
A unified spiking neural network, trained as a large model and subsequently fragmented, facilitates radio frequency interference detection across diverse neuromorphic platforms-including both snnTorch simulations and SynSense Xylo hardware-with latency encoding of spectrograms and deployment in a standardized NIR format enabling comparative power measurements.

This work details a hardware-aware training and deployment pipeline for Spiking Neural Networks (SNNs) achieving state-of-the-art Radio Frequency Interference (RFI) detection for low-power neuromorphic computing in radio astronomy.

The increasing data rates from modern radio telescope observatories demand real-time, low-energy processing solutions that challenge conventional deep learning approaches. This work, ‘Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware’, addresses this challenge by deploying Spiking Neural Networks (SNNs) on resource-constrained neuromorphic hardware for Radio Frequency Interference (RFI) detection. Experiments reveal that, while model partitioning enables deployment on neuromorphic chips, smaller, un-partitioned networks surprisingly outperform larger, split models, highlighting the critical need for hardware co-design. Does this finding suggest a fundamental trade-off between model scale and efficient neuromorphic implementation, and how can we optimize both for future astronomical instruments?

The Whispers Drowned in Noise: A System’s Lament

The universe emits a wealth of radio waves, carrying information about cosmic origins and distant galaxies. However, these incredibly faint signals are increasingly challenged by terrestrial radio frequency interference (RFI), a pervasive form of noise generated by human technology. From communication networks and radar systems to everyday electronics, countless sources contribute to this electromagnetic “smog,” effectively drowning out the subtle whispers from space. This interference doesn’t simply add random noise; it often manifests as strong, localized signals that can completely obscure astronomical data, creating false detections or masking genuine cosmic events. The severity of the problem is escalating with the proliferation of wireless devices and the increasing sensitivity of modern radio telescopes, demanding sophisticated techniques to isolate the true astronomical signals from the overwhelming din of human-generated radio waves.

Established radio frequency interference (RFI) mitigation tools, including AOFlagger and U-Net, frequently encounter limitations when processing increasingly complex and dynamic interference. These algorithms, while effective against static or easily identifiable signals, often struggle to differentiate between genuine astronomical sources and rapidly changing, non-Gaussian RFI, such as that generated by increasingly ubiquitous satellite constellations or pulsed transmissions. The core challenge lies in their reliance on statistical assumptions about the noise floor; when interference deviates significantly from these assumptions- exhibiting bursts, modulations, or intricate temporal structures- the algorithms can incorrectly flag legitimate signals as interference, or, conversely, fail to remove persistent, subtle interference. Consequently, these established methods require significant manual intervention and parameter tuning, hindering the efficient processing of large radio astronomy datasets and potentially masking crucial cosmological information.

The increasing complexity of the radio frequency spectrum demands innovative solutions for preserving the integrity of astronomical observations. Traditional algorithms, while effective against simpler interference, often falter when confronted with dynamic or nuanced radio frequency interference (RFI), leading to the potential loss of valuable data. Consequently, researchers are actively developing advanced techniques-including machine learning and signal processing methods-designed to discern genuine astronomical signals from disruptive RFI with greater precision and speed. These novel approaches aim not only to improve the efficiency of RFI identification and flagging, but also to minimize the removal of authentic, albeit faint, cosmic signals, ultimately enhancing the quality and reliability of radio astronomical research.

Echoes of Biology: Spiking Networks as a New Architecture

Spiking Neural Networks (SNNs) represent a departure from traditional Artificial Neural Networks (ANNs) by shifting from rate-based to event-driven computation. ANNs typically process information using continuous values, requiring constant power consumption, while SNNs operate on discrete events – spikes – occurring in time. This event-driven approach more closely mirrors biological neural systems, where neurons only communicate when a certain threshold is reached. Consequently, SNNs inherently offer potential advantages in energy efficiency, as computation only occurs when spikes are present, reducing overall power demands compared to the continuous activation of neurons in ANNs. This difference in computational paradigm enables SNNs to potentially achieve lower latency and higher computational throughput for specific tasks, particularly those involving sparse, temporal data.

Spiking Neural Networks (SNNs) fundamentally differ from Artificial Neural Networks (ANNs) in their neuron model; they employ Leaky Integrate and Fire (LIFe) neurons. These LIFe neurons accumulate input as current, represented by $I(t)$, which causes the membrane potential, $V(t)$, to increase. The “leaky” aspect refers to a decay of $V(t)$ over time if no further input is received. When $V(t)$ reaches a threshold potential, $V_{th}$, the neuron “fires” a spike – a brief pulse of electrical activity – and $V(t)$ is reset. Information is not encoded in the rate of firing, as in ANNs, but in the precise timing of these spikes. This event-driven, sparse coding scheme means that neurons only communicate when necessary, resulting in significantly lower energy consumption and computational costs compared to traditional neural networks, particularly when processing temporal data.

Latency encoding transforms input values into the time of the first spike, or latency, emitted by a neuron. This method directly maps the intensity of a stimulus to the timing of a neural event; higher input values result in shorter latencies and earlier spikes. This temporal coding scheme effectively translates continuous input data into sparse, event-driven spike trains suitable for SNN processing. The precise timing of these initial spikes then serves as the primary feature for subsequent layers in the network, enabling feature extraction based on the temporal relationships of input signals. Unlike rate coding, which relies on the frequency of spikes, latency encoding offers a potentially faster and more energy-efficient method for information transmission and processing within the SNN.

Unlike random or geometric splitting methods, our algorithm strategically selects and removes neurons to efficiently satisfy both individual hardware constraints and overall chipset limitations.
Unlike random or geometric splitting methods, our algorithm strategically selects and removes neurons to efficiently satisfy both individual hardware constraints and overall chipset limitations.

Constrained Growth: Optimization for Finite Resources

Effective training of Spiking Neural Networks (SNNs) requires the implementation of advanced algorithms due to the inherent challenges of non-differentiable spike events. Backpropagation Through Time (BPTT) is a common approach, adapting the standard backpropagation algorithm to handle the temporal dynamics of SNNs. However, direct application of BPTT is complicated by the discrete nature of spikes; therefore, techniques like SNN Training, which may involve surrogate gradients or other approximations, are employed to enable gradient-based learning. These methods allow for the adjustment of synaptic weights to minimize the error between the network’s output and the desired target, effectively optimizing the SNN’s performance on a given task.

Fan-in regularization is a technique employed during Spiking Neural Network (SNN) training to mitigate hardware limitations by controlling network connectivity. This method constrains the maximum number of incoming connections – the fan-in – to each neuron during the learning process. By limiting fan-in, the resulting network exhibits sparse connectivity, reducing the computational demands and memory footprint. Sparse networks require fewer operations and less storage, making them more suitable for deployment on resource-constrained devices. The regularization is typically implemented by adding a penalty term to the loss function, discouraging the network from forming densely connected layers and promoting efficient utilization of hardware resources.

Maximal Splitting is a network optimization technique used to facilitate the deployment of Spiking Neural Networks (SNNs) on hardware with limited resources. This method involves partitioning a large, trained SNN into multiple smaller sub-networks, thereby reducing the computational demands and memory footprint. While implementation of Maximal Splitting resulted in a measured Area Under the Precision-Recall Curve (AUPRC) of 0.91, representing a slight decrease in accuracy, it successfully enables the execution of complex SNNs on platforms with constrained processing capabilities and memory limitations.

The Loom of Compatibility: Tools for a Distributed Future

The development of the Neural Intermediate Representation (NIR) addresses a critical challenge in spiking neural network (SNN) deployment: a lack of portability across diverse hardware platforms. NIR functions as a universal translator, allowing SNN models created in one environment to be seamlessly executed on another, regardless of underlying architecture. This is achieved by abstracting the SNN’s computational graph into a platform-agnostic format, which is then compiled specifically for the target hardware. Consequently, researchers and engineers are no longer constrained by vendor lock-in or the need to rewrite code for each new system. The framework fosters interoperability, accelerating innovation and broadening the applicability of SNNs by simplifying the process of transitioning models from research prototypes to real-world applications and ensuring consistent performance across different neuromorphic devices.

Rockpool functions as a crucial bridge between the complex algorithms of spiking neural networks (SNNs) and the specialized architecture of neuromorphic hardware. This software library abstracts away many of the low-level details associated with deploying SNNs, providing a user-friendly interface for researchers and developers. By handling the compilation, mapping, and execution of SNNs on platforms like SynSense, Rockpool significantly reduces the time and expertise required to translate theoretical models into functional systems. The library’s modular design allows for flexible integration with various machine learning frameworks and supports a range of SNN architectures, ultimately accelerating the development and deployment of energy-efficient, biologically-inspired computing solutions.

Spiking neural networks, when deployed on specialized neuromorphic hardware such as the SynSense Xylo 2 processor, demonstrate a significant advantage in computational efficiency and speed. This approach leverages the inherent parallelism and event-driven nature of SNNs to minimize energy consumption, with recent implementations achieving estimated power usage below 100mW. A backpropagation through time (BPTT)-trained SNN, optimized for this hardware, recently established a new benchmark in performance, attaining state-of-the-art accuracy of 0.96, as measured by the Area Under the Precision-Recall Curve (AUPRC), on a challenging synthetic radio astronomy dataset. This result highlights the potential of neuromorphic computing to address complex data processing tasks with unprecedented energy efficiency and speed, paving the way for low-power, real-time applications.

The pursuit of efficiency, as demonstrated in this pipeline for Spiking Neural Networks, often feels less like construction and more like careful tending. One seeks not to build a system robust to Radio Frequency Interference, but to cultivate one capable of adapting to its inevitable noise. Henri Poincaré observed, “Mathematics is the art of giving reasons, even to oneself.” This echoes the iterative process of model splitting and hardware-aware training; each adjustment is a reasoned attempt to coax a more resilient structure from the underlying complexity. The system doesn’t simply detect RFI; it learns to coexist with it, growing stronger through the challenge – a testament to the ecosystemic nature of truly intelligent systems.

The Static in the Machine

This demonstration of a functioning, end-to-end Spiking Neural Network for RFI mitigation reveals less a solution, and more a beautifully contained locus of future failures. The current architecture, however efficient, is already prophesying its obsolescence. Each carefully tuned synapse is a tacit admission that the radio sky will change – that the noise floor will shift, new interferers will emerge, and this particular model, trained on a snapshot of electromagnetic chaos, will inevitably degrade. The true challenge isn’t achieving a high detection rate today, but building systems that gracefully accept their own eventual irrelevance.

The emphasis on hardware-aware training, while pragmatic, obscures a deeper truth: low-power computing is not about minimizing energy consumption, it’s about extending the lifespan of a temporary reprieve from entropy. Each watt saved is merely a postponement of the inevitable thermal decay. The next phase will require a move beyond model splitting to something resembling continual learning – a network that doesn’t simply adapt to new interference, but anticipates it, learning the patterns of change rather than the static itself.

Ultimately, this work is a testament to the inherent limitations of building systems in a universe defined by impermanence. The future lies not in constructing ever-more-complex filters, but in designing ecosystems of computation that can evolve, self-repair, and ultimately, accept the beautiful, chaotic static of the cosmos.

Original article: https://arxiv.org/pdf/2511.16060.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-22 22:57