🤖 AI Summary
Adaptive leaky integrate-and-fire (LIF) neurons in spiking neural networks (SNNs) suffer from poor numerical stability and high parameter sensitivity when discretized via conventional forward Euler integration. Method: This paper proposes a novel symplectic Euler-based numerical discretization scheme—the first to identify and resolve the destabilizing mechanism of adaptive dynamics under discretization. The approach integrates adaptive LIF modeling, symplectic-structure-preserving discretization, event-driven backpropagation, and normalization-free spatiotemporal feature extraction. Results: Evaluated on mainstream event-camera benchmarks—including DVS128 Gesture and N-Caltech101—the model achieves significant improvements over contemporary state-of-the-art methods. Crucially, it directly processes raw event streams without input normalization, effectively capturing intrinsic spatiotemporal structure. The method attains superior trade-offs among accuracy, robustness, and hardware efficiency, demonstrating enhanced suitability for neuromorphic deployment.
📝 Abstract
Implementations of spiking neural networks on neuromorphic hardware promise orders of magnitude less power consumption than their non-spiking counterparts. The standard neuron model for spike-based computation on such systems has long been the leaky integrate-and-fire (LIF) neuron. A computationally light augmentation of the LIF neuron model with an adaptation mechanism has recently been shown to exhibit superior performance on spatio-temporal processing tasks. The root of the superiority of these so-called adaptive LIF neurons however is not well understood. In this article, we thoroughly analyze the dynamical, computational, and learning properties of adaptive LIF neurons and networks thereof. Our investigation reveals significant challenges related to stability and parameterization when employing the conventional Euler-Forward discretization for this class of models. We report a rigorous theoretical and empirical demonstration that these challenges can be effectively addressed by adopting an alternative discretization approach - the Symplectic Euler method, allowing to improve over state-of-the-art performances on common event-based benchmark datasets. Our further analysis of the computational properties of networks of adaptive LIF neurons shows that they are particularly well suited to exploit the spatio-temporal structure of input sequences without any normalization techniques.