Lynxi Technologies, Beijing 100097, China.
School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.
Neural Netw. 2022 May;149:184-194. doi: 10.1016/j.neunet.2022.02.006. Epub 2022 Feb 11.
Bio-inspired recipes are being introduced to artificial neural networks for the efficient processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is the most remarkable one thanks to its temporal processing capability, lightweight model structure, and well investigated direct training methods. However, most learnable LIF networks generally take neurons as independent individuals that communicate via chemical synapses, leaving electrical synapses all behind. On the contrary, it has been well investigated in biological neural networks that the inter-neuron electrical synapse takes a great effect on the coordination and synchronization of generating action potentials. In this work, we are engaged in modeling such electrical synapses in artificial LIF neurons, where membrane potentials propagate to neighbor neurons via convolution operations, and the refined neural model ECLIF is proposed. We then build deep networks using ECLIF and trained them using a back-propagation-through-time algorithm. We found that the proposed network has great accuracy improvement over traditional LIF on five datasets and achieves high accuracy on them. In conclusion, it reveals that the introduction of the electrical synapse is an important factor for achieving high accuracy on realistic spatio-temporal tasks.
生物启发型食谱正被引入人工神经网络，以实现时空任务的高效处理。其中，Leaky Integrate and Fire (LIF) 模型因其具有时间处理能力、轻量级模型结构和经过深入研究的直接训练方法而引人注目。然而，大多数可学习的 LIF 网络通常将神经元视为独立的个体，通过化学突触进行通信，而忽略了电突触。相反，在生物神经网络中已经有大量研究表明，神经元之间的电突触对产生动作电位的协调和同步有很大的影响。在这项工作中，我们致力于在人工 LIF 神经元中模拟这种电突触，其中膜电位通过卷积操作传播到相邻神经元，并且提出了改进的神经模型 ECLIF。然后，我们使用 ECLIF 构建深度网络，并使用反向传播时间算法进行训练。我们发现，与传统的 LIF 相比，所提出的网络在五个数据集上具有更高的准确性，并且在这些数据集上取得了很高的准确性。总之，这表明引入电突触是在现实时空任务中实现高精度的重要因素。
