DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks

• URL: http://arxiv.org/abs/2502.10422v1
• Date: Wed, 05 Feb 2025 09:02:07 GMT
• Title: DA-LIF: Dual Adaptive Leaky Integrate-and-Fire Model for Deep Spiking Neural Networks
• Authors: Tianqing Zhang, Kairong Yu, Jian Zhang, Hongwei Wang,
• Abstract summary: Spiking Neural Networks (SNNs) are valued for their ability to process-temporal information efficiently.<n>We propose the Dual Leaky Integrate-and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays.
• Score: 5.832445095443944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking Neural Networks (SNNs) are valued for their ability to process spatio-temporal information efficiently, offering biological plausibility, low energy consumption, and compatibility with neuromorphic hardware. However, the commonly used Leaky Integrate-and-Fire (LIF) model overlooks neuron heterogeneity and independently processes spatial and temporal information, limiting the expressive power of SNNs. In this paper, we propose the Dual Adaptive Leaky Integrate-and-Fire (DA-LIF) model, which introduces spatial and temporal tuning with independently learnable decays. Evaluations on both static (CIFAR10/100, ImageNet) and neuromorphic datasets (CIFAR10-DVS, DVS128 Gesture) demonstrate superior accuracy with fewer timesteps compared to state-of-the-art methods. Importantly, DA-LIF achieves these improvements with minimal additional parameters, maintaining low energy consumption. Extensive ablation studies further highlight the robustness and effectiveness of the DA-LIF model.

Related papers

• 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)
• SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network [19.73335869722781]
  Continuous learning of novel classes is crucial for edge devices to preserve data privacy and maintain reliable performance in dynamic environments.<n>In this work, we present an SNN-based method for On-Device FSCIL ie., Sparsity-Aware and Fast Adaptive SNN.
  arXiv  Detail & Related papers  (2025-10-04T03:21:31Z)
• Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training [63.3991315762955]
  Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation.<n>Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics.<n>We propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics.
  arXiv  Detail & Related papers  (2025-07-22T18:20:56Z)
• 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)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting [27.91825785119938]
  Spiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing data for time series forecasting. We introduce the Temporal Leaky Segment Integrate-and-Fire model, featuring a dual-compartment architecture. Experimental results show that TS-LIF outperforms traditional SNNs in time series forecasting.
  arXiv  Detail & Related papers  (2025-03-07T03:06:21Z)
• Autaptic Synaptic Circuit Enhances Spatio-temporal Predictive Learning of Spiking Neural Networks [23.613277062707844]
  Spiking Neural Networks (SNNs) emulate the integrated-fire-leak mechanism found in biological neurons. Existing SNNs predominantly rely on the Integrate-and-Fire Leaky (LIF) model. This paper proposes a novel S-patioTemporal Circuit (STC) model.
  arXiv  Detail & Related papers  (2024-06-01T11:17:27Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• Fully Spiking Denoising Diffusion Implicit Models [61.32076130121347]
  Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds. We propose a novel approach fully spiking denoising diffusion implicit model (FSDDIM) to construct a diffusion model within SNNs. We demonstrate that the proposed method outperforms the state-of-the-art fully spiking generative model.
  arXiv  Detail & Related papers  (2023-12-04T09:07:09Z)
• Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding [32.25788551849627]
  Brain-inspired spiking neural networks (SNNs) have struggled to match their biological counterpart in modeling long-term temporal relationships. This paper presents a novel Contextual Embedding Leaky Integrate-and-Fire (CE-LIF) spiking neuron model.
  arXiv  Detail & Related papers  (2023-08-29T09:33:10Z)
• TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential Modelling [54.97005925277638]
  The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. It remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues. We propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF.
  arXiv  Detail & Related papers  (2023-08-25T08:54:41Z)
• 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)
• A Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• Spikformer: When Spiking Neural Network Meets Transformer [102.91330530210037]
  We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the self-attention mechanism. We propose a novel Spiking Self Attention (SSA) as well as a powerful framework, named Spiking Transformer (Spikformer)
  arXiv  Detail & Related papers  (2022-09-29T14:16:49Z)
• Modeling Associative Plasticity between Synapses to Enhance Learning of Spiking Neural Networks [4.736525128377909]
  Spiking Neural Networks (SNNs) are the third generation of artificial neural networks that enable energy-efficient implementation on neuromorphic hardware. We propose a robust and effective learning mechanism by modeling the associative plasticity between synapses. Our approaches achieve superior performance on static and state-of-the-art neuromorphic datasets.
  arXiv  Detail & Related papers  (2022-07-24T06:12:23Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)

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.