Spiking neural networks play an important role in brain-like neuromorphic computations and in studying working mechanisms of neural circuits. One drawback of training a large scale spiking neural network is that an expensive cost of updating all weights is required. Furthermore, after training, all information related to the computational task is hidden into the weight matrix, prohibiting us from a transparent understanding of circuit mechanisms. Therefore, in this work, we address these challenges by proposing a spiking mode-based training protocol. The first advantage is that the weight is interpreted by input and output modes and their associated scores characterizing importance of each decomposition term. The number of modes is thus adjustable, allowing more degrees of freedom for modeling the experimental data. This reduces a sizable training cost because of significantly reduced space complexity for learning. The second advantage is that one can project the high dimensional neural activity in the ambient space onto the mode space which is typically of a low dimension, e.g., a few modes are sufficient to capture the shape of the underlying neural manifolds. We analyze our framework in two computational tasks -- digit classification and selective sensory integration tasks. Our work thus derives a mode-based learning rule for spiking neural networks.

翻译：脉冲神经网络在类脑神经形态计算及神经回路工作机制研究中具有重要作用。训练大规模脉冲神经网络的一个缺陷是需要高昂的成本来更新所有权重。此外，训练完成后，与计算任务相关的所有信息都隐藏于权重矩阵中，阻碍了我们透明地理解回路机制。因此，本研究通过提出一种基于脉冲模态的训练协议来应对这些挑战。其首要优势在于，权重由输入和输出模态及其表征各分解项重要性的关联得分共同解释。模态数量可调节，从而为实验数据建模提供更多自由度。由于显著降低了学习所需的空间复杂度，这使得训练成本大幅缩减。第二个优势在于，可以将环境空间中的高维神经活动投影到通常为低维的模态空间（例如，少数几个模态即可捕捉底层神经流形的形态）。我们通过数字分类与选择性感觉整合两项计算任务对该框架进行了分析。由此，本研究推导出适用于脉冲神经网络的基于模态的学习规则。