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.

通过提出基于脉冲模式的训练协议，解决了大规模脉冲神经网络更新权重代价高、隐藏计算任务信息、难以理解神经电路机制等问题，引入模式空间降低训练代价并捕捉底层神经流形结构，得到适用于脉冲神经网络的基于模式的学习规则。