Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks

• URL: http://arxiv.org/abs/2401.03719v1
• Date: Mon, 8 Jan 2024 08:05:34 GMT
• Title: Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks
• Authors: Qi Xu, Yuyuan Gao, Jiangrong Shen, Yaxin Li, Xuming Ran, Huajin Tang, Gang Pan
• Abstract summary: Spiking neural networks (SNNs) serve as one type of efficient model to processtemporal-temporal patterns in time series. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional attention module (SCBAM) component. It invokes the history information in spatial and temporal channels adaptively through SCBAM which brings the advantages of efficient memory calling history and redundancy elimination.
• Score: 21.509659756334802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spiking neural networks (SNNs) serve as one type of efficient model to process spatio-temporal patterns in time series, such as the Address-Event Representation data collected from Dynamic Vision Sensor (DVS). Although convolutional SNNs have achieved remarkable performance on these AER datasets, benefiting from the predominant spatial feature extraction ability of convolutional structure, they ignore temporal features related to sequential time points. In this paper, we develop a recurrent spiking neural network (RSNN) model embedded with an advanced spiking convolutional block attention module (SCBAM) component to combine both spatial and temporal features of spatio-temporal patterns. It invokes the history information in spatial and temporal channels adaptively through SCBAM, which brings the advantages of efficient memory calling and history redundancy elimination. The performance of our model was evaluated in DVS128-Gesture dataset and other time-series datasets. The experimental results show that the proposed SRNN-SCBAM model makes better use of the history information in spatial and temporal dimensions with less memory space, and achieves higher accuracy compared to other models.

Related papers

• DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs [59.434893231950205]
  Dynamic graph learning aims to uncover evolutionary laws in real-world systems. We propose DyG-Mamba, a new continuous state space model for dynamic graph learning. We show that DyG-Mamba achieves state-of-the-art performance on most datasets.
  arXiv  Detail & Related papers  (2024-08-13T15:21:46Z)
• Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation [5.764710042535229]
  This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) This design captures and utilizes temporal dependencies, significantly enhancing the generative capabilities of SNNs. Our approach excels in generating high-resolution and temporally consistent data, underscoring the crucial role of temporal information in SNN-based generative modeling.
  arXiv  Detail & Related papers  (2024-05-23T12:04:46Z)
• Efficient and Effective Time-Series Forecasting with Spiking Neural Networks [47.371024581669516]
  Spiking neural networks (SNNs) provide a unique pathway for capturing the intricacies of temporal data. Applying SNNs to time-series forecasting is challenging due to difficulties in effective temporal alignment, complexities in encoding processes, and the absence of standardized guidelines for model selection. We propose a framework for SNNs in time-series forecasting tasks, leveraging the efficiency of spiking neurons in processing temporal information.
  arXiv  Detail & Related papers  (2024-02-02T16:23:50Z)
• Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting [52.47493322446537]
  We develop a adaptive, interpretable and scalable forecasting framework, which seeks to individually model each component of the spatial-temporal patterns. SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns. Extensive experiments are conducted to demonstrate that SCNN can achieve superior performance over state-of-the-art models on three real-world datasets.
  arXiv  Detail & Related papers  (2023-05-22T13:39:44Z)
• STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks [7.422913384086416]
  Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal processing capability. Existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither which can extract temporal dependencies adequately. We take inspiration from biological synapses and propose a synaptic connection SNN model, to enhance the synapse-temporal receptive fields of synaptic connections. We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks.
  arXiv  Detail & Related papers  (2022-10-11T08:13:22Z)
• An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning [14.353798949041698]
  We propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning. The core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module.
  arXiv  Detail & Related papers  (2022-06-07T16:12:17Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data [1.8352113484137622]
  In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags. It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction.
  arXiv  Detail & Related papers  (2021-04-08T12:43:44Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network [7.503685643036081]
  A bio-plausible SNN model with spatial-temporal property is a complex dynamic system. We formulate SNN as a network of infinite impulse response (IIR) filters with neuron nonlinearity. We propose a training algorithm that is capable to learn spatial-temporal patterns by searching for the optimal synapse filter kernels and weights.
  arXiv  Detail & Related papers  (2020-02-19T01:27:39Z)

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.