Sustainable AI: Mathematical Foundations of Spiking Neural Networks

• URL: http://arxiv.org/abs/2503.02013v1
• Date: Mon, 03 Mar 2025 19:44:12 GMT
• Title: Sustainable AI: Mathematical Foundations of Spiking Neural Networks
• Authors: Adalbert Fono, Manjot Singh, Ernesto Araya, Philipp C. Petersen, Holger Boche, Gitta Kutyniok,
• Abstract summary: Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains.<n>This article examines the computational properties of spiking networks through the lens of learning theory.
• Score: 46.76155269576732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning's success comes with growing energy demands, raising concerns about the long-term sustainability of the field. Spiking neural networks, inspired by biological neurons, offer a promising alternative with potential computational and energy-efficiency gains. This article examines the computational properties of spiking networks through the lens of learning theory, focusing on expressivity, training, and generalization, as well as energy-efficient implementations while comparing them to artificial neural networks. By categorizing spiking models based on time representation and information encoding, we highlight their strengths, challenges, and potential as an alternative computational paradigm.

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