2 research outputs found
A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
Spiking neural networks (SNNs) have demonstrated excellent capabilities in
various intelligent scenarios. Most existing methods for training SNNs are
based on the concept of synaptic plasticity; however, learning in the realistic
brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike
threshold of biological neurons is a critical intrinsic neuronal feature that
exhibits rich dynamics on a millisecond timescale and has been proposed as an
underlying mechanism that facilitates neural information processing. In this
study, we develop a novel synergistic learning approach that simultaneously
trains synaptic weights and spike thresholds in SNNs. SNNs trained with
synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher
accuracies on various static and neuromorphic datasets than SNNs trained with
two single-learning models of the synaptic learning (SL) and the threshold
learning (TL). During training, the synergistic learning approach optimizes
neural thresholds, providing the network with stable signal transmission via
appropriate firing rates. Further analysis indicates that STL-SNNs are robust
to noisy data and exhibit low energy consumption for deep network structures.
Additionally, the performance of STL-SNN can be further improved by introducing
a generalized joint decision framework (JDF). Overall, our findings indicate
that biologically plausible synergies between synaptic and intrinsic
non-synaptic mechanisms may provide a promising approach for developing highly
efficient SNN learning methods.Comment: 13 pages, 9 figures, submitted for publicatio
A Spatial-channel-temporal-fused Attention for Spiking Neural Networks
Spiking neural networks (SNNs) mimic brain computational strategies, and
exhibit substantial capabilities in spatiotemporal information processing. As
an essential factor for human perception, visual attention refers to the
dynamic selection process of salient regions in biological vision systems.
Although mechanisms of visual attention have achieved great success in computer
vision, they are rarely introduced into SNNs. Inspired by experimental
observations on predictive attentional remapping, we here propose a new
spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to
efficiently capture underlying target regions by utilizing historically
accumulated spatial-channel information. Through a systematic evaluation on
three event stream datasets (DVS Gesture, SL-Animals-DVS and MNIST-DVS), we
demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only
significantly outperforms the baseline SNN (BL-SNN) and other two SNN models
with degenerated attention modules, but also achieves competitive accuracy with
existing state-of-the-art methods. Additionally, our detailed analysis shows
that the proposed SCTFA-SNN model has strong robustness to noise and
outstanding stability to incomplete data, while maintaining acceptable
complexity and efficiency. Overall, these findings indicate that appropriately
incorporating cognitive mechanisms of the brain may provide a promising
approach to elevate the capability of SNNs.Comment: 12 pages, 8 figures, 5 tabes; This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl