Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks

• URL: http://arxiv.org/abs/2603.20687v1
• Date: Sat, 21 Mar 2026 07:06:57 GMT
• Title: Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks
• Abstract summary: Spiking Neural Networks (SNNs) are promising for energy-efficient, real-time edge computing.<n>Existing LIF models struggle with restricted information capacity and susceptibility to noise.<n>Inspired by the dynamic self-regulation of biological potassium channels, we propose the Potassium-regulated LIF (KvLIF) neuron model.
• Score: 4.9720552049794575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Neural Dynamics Self-Attention for Spiking Transformers [59.57278823240336]
  Spiking Neural Networks (SNNs) with Transformer architectures offer a promising pathway to balance energy efficiency and performance.<n>Existing Spiking Transformers face two critical challenges: (i) a substantial performance gap compared to their Artificial Neural Networks (ANNs) counterparts and (ii) high memory overhead during inference.<n>We propose LRF-Dyn, which uses spiking neurons with localized receptive fields to compute attention while reducing memory requirements.
  arXiv  Detail & Related papers  (2026-03-09T12:23:37Z)
• General Self-Prediction Enhancement for Spiking Neurons [71.01912385372577]
  Spiking Neural Networks (SNNs) are highly energy-efficient due to event-driven, sparse computation, but their training is challenged by spike non-differentiability and trade-offs among performance, efficiency, and biological plausibility.<n>We propose a self-prediction enhanced spiking neuron method that generates an internal prediction current from its input-output history to modulate membrane potential.<n>This design offers dual advantages, it creates a continuous gradient path that alleviates vanishing gradients and boosts training stability and accuracy, while also aligning with biological principles, which resembles distal dendritic modulation and error-driven synaptic plasticity.
  arXiv  Detail & Related papers  (2026-01-29T15:08:48Z)
• Discretized Quadratic Integrate-and-Fire Neuron Model for Deep Spiking Neural Networks [0.08749675983608168]
  Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to traditional artificial neural networks.<n>We propose the first discretization of the Quadratic Integrate-and-Fire (QIF) neuron model tailored for high-performance deep spiking neural networks.
  arXiv  Detail & Related papers  (2025-10-05T02:30:10Z)
• Training Deep Normalization-Free Spiking Neural Networks with Lateral Inhibition [52.59263087086756]
  Training deep neural networks (SNNs) has critically depended on explicit normalization schemes, such as batch normalization.<n>We propose a normalization-free learning framework that incorporates lateral inhibition inspired by cortical circuits.<n>We show that our framework enables stable training of deep SNNs with biological realism and achieves competitive performance without resorting to explicit normalizations.
  arXiv  Detail & Related papers  (2025-09-27T11:11:30Z)
• Incorporating the Refractory Period into Spiking Neural Networks through Spike-Triggered Threshold Dynamics [16.273350447266132]
  We propose a method to incorporate the refractory period into spiking LIF neurons through spike-triggered threshold dynamics.<n> RPLIF achieves state-of-the-art performance on Cifar10-DVS(82.40%) and N-Caltech101(83.35%) with fewer timesteps and demonstrates superior performance on DVS128 Gesture(97.22%) at low latency.
  arXiv  Detail & Related papers  (2025-09-22T13:33:31Z)
• Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling [66.0841376808143]
  Dendritic Resonate-and-Fire (RF) neurons can efficiently extract frequency from input signals and encode them into spike trains.<n>RF neurons exhibit limited effective memory capacity and a trade-off between energy efficiency and training speed on complex tasks.<n>We propose a Dendritic Resonate-and-Fire (D-RF) model, which explicitly incorporates a multi-dendritic and soma architecture.
  arXiv  Detail & Related papers  (2025-09-21T18:15:45Z)
• A Brain-Inspired Gating Mechanism Unlocks Robust Computation in Spiking Neural Networks [5.647576619206974]
  We introduce the Dynamic Gated Neuron(DGN), a novel spiking unit in which membrane conductance evolves in response to neuronal activity.<n>Our results highlight, for the first time, a biologically plausible dynamic gating as a key mechanism for robust spike-based computation.
  arXiv  Detail & Related papers  (2025-09-03T13:00:49Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks [1.9775291915550175]
  Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures.<n>We develop a scalable probabilistic spiking learning framework for long-range dependency tasks.<n>Our models attain state-of-the-art performance among SNN models across diverse long-range dependency tasks.
  arXiv  Detail & Related papers  (2024-06-05T04:23:11Z)
• Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns [4.8416725611508244]
  Spiking Neural Networks (SNNs) use discrete spike sequences to transmit information, which significantly mimics the information transmission of the brain. This paper introduces the dynamic Burst pattern and designs the Leaky Integrate and Fire or Burst (LIFB) neuron that can make a trade-off between short-time performance and dynamic temporal performance.
  arXiv  Detail & Related papers  (2023-01-29T04:22:07Z)
• Continuous Learning and Adaptation with Membrane Potential and Activation Threshold Homeostasis [91.3755431537592]
  This paper presents the Membrane Potential and Activation Threshold Homeostasis (MPATH) neuron model. The model allows neurons to maintain a form of dynamic equilibrium by automatically regulating their activity when presented with input. Experiments demonstrate the model's ability to adapt to and continually learn from its input.
  arXiv  Detail & Related papers  (2021-04-22T04:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.