Rethinking Spiking Neural Networks from an Ensemble Learning Perspective

• URL: http://arxiv.org/abs/2502.14218v1
• Date: Thu, 20 Feb 2025 03:15:52 GMT
• Title: Rethinking Spiking Neural Networks from an Ensemble Learning Perspective
• Authors: Yongqi Ding, Lin Zuo, Mengmeng Jing, Pei He, Hanpu Deng,
• Abstract summary: Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporalworks that share architectures and weights. We promote the consistency of the initial membrane potential distribution and output through membrane potential smoothing and temporally adjacent subnetwork guidance.
• Score: 4.823440259626247
• License:
• Abstract: Spiking neural networks (SNNs) exhibit superior energy efficiency but suffer from limited performance. In this paper, we consider SNNs as ensembles of temporal subnetworks that share architectures and weights, and highlight a crucial issue that affects their performance: excessive differences in initial states (neuronal membrane potentials) across timesteps lead to unstable subnetwork outputs, resulting in degraded performance. To mitigate this, we promote the consistency of the initial membrane potential distribution and output through membrane potential smoothing and temporally adjacent subnetwork guidance, respectively, to improve overall stability and performance. Moreover, membrane potential smoothing facilitates forward propagation of information and backward propagation of gradients, mitigating the notorious temporal gradient vanishing problem. Our method requires only minimal modification of the spiking neurons without adapting the network structure, making our method generalizable and showing consistent performance gains in 1D speech, 2D object, and 3D point cloud recognition tasks. In particular, on the challenging CIFAR10-DVS dataset, we achieved 83.20\% accuracy with only four timesteps. This provides valuable insights into unleashing the potential of SNNs.

Related papers

• FSTA-SNN:Frequency-based Spatial-Temporal Attention Module for Spiking Neural Networks [6.185559627969663]
  Spiking Neural Networks (SNNs) are emerging as a promising alternative to Artificial Neural Networks (ANNs) In this work, we analyze inherent inherent characteristics of SNNs from both temporal and spatial perspectives. We propose a Frequency-based Spatial Attention (FSTA) module to enhance feature learning in SNNs.
  arXiv  Detail & Related papers  (2024-12-15T08:23:58Z)
• Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks [50.32980443749865]
  Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biologicalability. Current SNNs struggle to balance accuracy and latency in neuromorphic datasets. We propose Step-wise Distillation (HSD) method, tailored for neuromorphic datasets.
  arXiv  Detail & Related papers  (2024-09-19T06:52:34Z)
• Directly Training Temporal Spiking Neural Network with Sparse Surrogate Gradient [8.516243389583702]
  Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based computing and energy-efficient features. We propose Masked Surrogate Gradients (MSGs) to balance the effectiveness of training and the sparseness of the gradient, thereby improving the generalization ability of SNNs.
  arXiv  Detail & Related papers  (2024-06-28T04:21:32Z)
• Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks [19.490903216456758]
  Spiking neural networks (SNNs) are neural networks with asynchronous discrete and sparse characteristics. We propose a multi-level firing (MLF) method based on the existing spiking-suppressed residual network (spiking DS-ResNet)
  arXiv  Detail & Related papers  (2022-10-12T16:39:46Z)
• Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation [70.75043144299168]
  Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. It is a challenge to efficiently train SNNs due to their non-differentiability. We propose the Differentiation on Spike Representation (DSR) method, which could achieve high performance.
  arXiv  Detail & Related papers  (2022-05-01T12:44:49Z)
• Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer [77.78479877473899]
  We design a spatial-temporal-fusion BNN for efficiently scaling BNNs to large models. Compared to vanilla BNNs, our approach can greatly reduce the training time and the number of parameters, which contributes to scale BNNs efficiently.
  arXiv  Detail & Related papers  (2021-12-12T17:13:14Z)
• Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks [5.484391472233163]
  The success of deep learning is inseparable from backpropagation. We propose a biological plausible spatial adjustment, which rethinks the relationship between membrane potential and spikes. Secondly, we propose a biologically plausible temporal adjustment making the error propagate across the spikes in the temporal dimension.
  arXiv  Detail & Related papers  (2021-10-17T15:55:51Z)
• Distribution-sensitive Information Retention for Accurate Binary Neural Network [49.971345958676196]
  We present a novel Distribution-sensitive Information Retention Network (DIR-Net) to retain the information of the forward activations and backward gradients. Our DIR-Net consistently outperforms the SOTA binarization approaches under mainstream and compact architectures. We conduct our DIR-Net on real-world resource-limited devices which achieves 11.1 times storage saving and 5.4 times speedup.
  arXiv  Detail & Related papers  (2021-09-25T10:59:39Z)
• BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons [8.956708722109415]
  Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. We propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN) For the MNIST, FashionMNIST, and N-MNIST datasets, our model has achieved state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-05-27T08:38:31Z)
• Revisiting Initialization of Neural Networks [72.24615341588846]
  We propose a rigorous estimation of the global curvature of weights across layers by approximating and controlling the norm of their Hessian matrix. Our experiments on Word2Vec and the MNIST/CIFAR image classification tasks confirm that tracking the Hessian norm is a useful diagnostic tool.
  arXiv  Detail & Related papers  (2020-04-20T18:12:56Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

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.