Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks
arXiv:2603.20687v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient, real-time edge computing, yet their performance is often constrained by the limited adaptability of conventional leaky integrate-and-fire (LIF) neurons. Existing LIF models struggle with restricted information capacity and susceptibility to noise, leading to degraded accuracy and compromised robustness. Inspired by the dynamic self-regulation of biological potassium channels, we propose the Potassium-regulated LIF (KvLIF) neuron model. KvLIF introduces an auxiliary conductance state that integrates membrane potential and spiking history to adaptively modulate neuronal excitability and reset dynamics. This design extends the dynamic response range of neurons to varying input intensities and effectively suppresses noise-induced spikes. We extensively evaluate KvLIF on both static image and neuromorphic datasets, demonstrating consistent improvements in classification accura
arXiv:2603.20687v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) are promising for energy-efficient, real-time edge computing, yet their performance is often constrained by the limited adaptability of conventional leaky integrate-and-fire (LIF) neurons. Existing LIF models struggle with restricted information capacity and susceptibility to noise, leading to degraded accuracy and compromised robustness. Inspired by the dynamic self-regulation of biological potassium channels, we propose the Potassium-regulated LIF (KvLIF) neuron model. KvLIF introduces an auxiliary conductance state that integrates membrane potential and spiking history to adaptively modulate neuronal excitability and reset dynamics. This design extends the dynamic response range of neurons to varying input intensities and effectively suppresses noise-induced spikes. We extensively evaluate KvLIF on both static image and neuromorphic datasets, demonstrating consistent improvements in classification accuracy and superior robustness compared to existing LIF models. Our work bridges biological plausibility with computational efficiency, offering a neuron model that enhances SNN performance while maintaining suitability for low-power neuromorphic deployment.
Executive Summary
This article proposes a novel neuron model, the Potassium-regulated LIF (KvLIF) neuron, which enhances the capacity and robustness of representation in Spiking Neural Networks (SNNs). Inspired by biological potassium channels, KvLIF introduces an auxiliary conductance state to adaptively modulate neuronal excitability and reset dynamics. Experimental results demonstrate improved classification accuracy and robustness compared to existing LIF models on both static image and neuromorphic datasets. The KvLIF model bridges biological plausibility with computational efficiency, making it suitable for low-power neuromorphic deployment. This work contributes to the advancement of SNNs for energy-efficient, real-time edge computing. Its findings have significant implications for applications in image processing, pattern recognition, and machine learning.
Key Points
- ▸ Introduction of the Potassium-regulated LIF (KvLIF) neuron model
- ▸ Adaptive modulation of neuronal excitability and reset dynamics
- ▸ Improved classification accuracy and robustness on various datasets
Merits
Strength in Biological Plausibility
The KvLIF model is inspired by biological potassium channels, making it a biologically plausible neuron model that enhances SNN performance.
Enhanced Computational Efficiency
The KvLIF model extends the dynamic response range of neurons to varying input intensities, effectively suppressing noise-induced spikes and improving computational efficiency.
Improved Robustness and Accuracy
Experimental results demonstrate consistent improvements in classification accuracy and superior robustness compared to existing LIF models on various datasets.
Demerits
Limited Generalizability
The KvLIF model may not be applicable to all types of neuromorphic datasets, and its performance may degrade in certain scenarios.
Complexity and Implementation
The KvLIF model introduces additional complexity to the traditional LIF neuron model, which may require significant modifications to existing SNN architectures and deployment frameworks.
Expert Commentary
The KvLIF model is a significant contribution to the field of SNNs, offering improved capacity and robustness of representation. While the model shows promise, its generalizability and implementation complexity require careful consideration. The findings of this work have significant practical implications for the development of energy-efficient, real-time edge computing systems, and may inform policy decisions related to the deployment of neuromorphic computing systems. Future work should focus on exploring the KvLIF model's performance on a wider range of datasets and scenarios, as well as its potential applications in real-world systems.
Recommendations
- ✓ Future work should explore the KvLIF model's performance on a wider range of datasets and scenarios.
- ✓ The development of more efficient and robust neural network architectures for edge computing should be a priority, particularly in applications such as image processing, pattern recognition, and machine learning.
Sources
Original: arXiv - cs.LG
