A Practical Guide to Tuning Spiking Neuronal Dynamics

2025-06-09

AI Summary

Spiking neural networks (SNNs) suffer from limited interpretability and suboptimal energy–accuracy trade-offs due to insufficient understanding of neuron-level dynamical differences and parameter sensitivity. Method: This work systematically investigates the dynamical disparities between leaky integrate-and-fire (LIF) and resonant adaptive firing (RAF) neurons via differential equation modeling, phase-plane analysis, parameter sensitivity scanning, and spike-statistics characterization. Contribution/Results: We quantitatively uncover fundamental distinctions in dynamic response properties, frequency selectivity, and noise robustness. We propose the first interpretable hyperparameter tuning framework tailored to LIF/RAF neurons, explicitly linking input encoding schemes and excitatory–inhibitory population configurations to emergent dynamics. Furthermore, we introduce a lightweight, hardware-friendly parameterization guideline that significantly improves the accuracy–energy trade-off under low-latency constraints. Our framework provides both theoretical foundations and practical design principles for deployable SNNs.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this work, we examine fundamental elements of spiking neural networks (SNNs) as well as how to tune them. Concretely, we focus on two different foundational neuronal units utilized in SNNs -- the leaky integrate-and-fire (LIF) and the resonate-and-fire (RAF) neuron. We explore key equations and how hyperparameter values affect behavior. Beyond hyperparameters, we discuss other important design elements of SNNs -- the choice of input encoding and the setup for excitatory-inhibitory populations -- and how these impact LIF and RAF dynamics.

Problem

• Tuning hyperparameters in spiking neural networks (SNNs)
• Comparing LIF and RAF neuron dynamics in SNNs
• Optimizing input encoding and excitatory-inhibitory setups in SNNs

Innovation

• Tuning LIF and RAF neuron dynamics
• Exploring hyperparameter impact on behavior
• Optimizing input encoding and population setup