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[2602.02306] Spark: Modular Spiking Neural Networks
Summary
The paper presents Spark, a modular framework for spiking neural networks aimed at improving data and energy efficiency in AI applications.
Why It Matters
As AI systems become increasingly complex, the need for efficient neural network models is critical. Spark addresses this by offering a modular approach to spiking neural networks, which could enhance learning algorithms and reduce resource consumption, making it relevant for ongoing AI research and development.
Key Takeaways
- Spark introduces a modular design for spiking neural networks.
- The framework aims to improve data efficiency and energy consumption.
- It showcases potential applications in continuous and unbatched learning.
- The approach could accelerate research in spiking neural networks.
- Effective plasticity mechanisms are essential for enhancing learning in these networks.
Computer Science > Neural and Evolutionary Computing arXiv:2602.02306 (cs) [Submitted on 2 Feb 2026 (v1), last revised 25 Feb 2026 (this version, v3)] Title:Spark: Modular Spiking Neural Networks Authors:Mario Franco, Carlos Gershenson View a PDF of the paper titled Spark: Modular Spiking Neural Networks, by Mario Franco and Carlos Gershenson View PDF HTML (experimental) Abstract:Nowadays, neural networks act as a synonym for artificial intelligence. Present neural network models, although remarkably powerful, are inefficient both in terms of data and energy. Several alternative forms of neural networks have been proposed to address some of these problems. Specifically, spiking neural networks are suitable for efficient hardware implementations. However, effective learning algorithms for spiking networks remain elusive, although it is suspected that effective plasticity mechanisms could alleviate the problem of data efficiency. Here, we present a new framework for spiking neural networks - Spark - built upon the idea of modular design, from simple components to entire models. The aim of this framework is to provide an efficient and streamlined pipeline for spiking neural networks. We showcase this framework by solving the sparse-reward cartpole problem with simple plasticity mechanisms. We hope that a framework compatible with traditional ML pipelines may accelerate research in the area, specifically for continuous and unbatched learning, akin to the one animals exhibit. S...