F. Khani, Y. Khakipoor and A. Ahmadi, A Cost-Efficient Chaotic Neuron for Learning Enhancement in Spiking Neural Networks, in IEEE Transactions on Emerging Topics in Computational Intelligence, doi: 10.1109/TETCI.2026.3716875.
Abstract: Spiking neural networks (SNNs) are increasingly valued for their ability to emulate the temporal dynamics of biological neurons within hardware-efficient architectures. However, commonly used neuron models, such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF), fall short in capturing the complex chaotic spiking-bursting behaviors found in biological neurons, which can accelerate learning and improve accuracy. To address these limitations, we introduce an optimized two-dimensional map-based neuron model (called the MR model), adapted from the Rulkov model, that efficiently achieves chaotic spiking-bursting behavior while minimizing computational demands. This model overcomes typical trade-offs between accuracy and efficiency, making it suitable for digital implementation in both hardware and software contexts. As a proof of concept, an FPGA-based implementation demonstrates the model’s resource efficiency. In image classification tasks, incorporating chaotic dynamics in only 1% of neurons resulted in up to 20% higher accuracy and a 50% reduction in convergence time compared to standard neuron models. These findings indicate that chaotic dynamics in SNNs can significantly enhance learning performance, providing an effective approach for real-time neuromorphic applications.
keywords: Modeling; Neurons; Dynamics; Hardware; Accuracy; Learning (artificial intelligence); Costing; Costs; Limit-cycles; Spiking neural networks; Pattern recognition; map-based neuron models; dynamic systems; spiking-bursting; chaotic regime; neuromorphic; FPGA; Spiking Neural Network (SNN),