  Spiking neural networks (SNNs) have superb characteristics in sensory
information recognition tasks due to their biological plausibility. However,
the performance of some current spiking-based models is limited by their
structures which means either fully connected or too-deep structures bring too
much redundancy. This redundancy from both connection and neurons is one of the
key factors hindering the practical application of SNNs. Although Some pruning
methods were proposed to tackle this problem, they normally ignored the fact
the neural topology in the human brain could be adjusted dynamically. Inspired
by this, this paper proposed an evolutionary-based structure construction
method for constructing more reasonable SNNs. By integrating the knowledge
distillation and connection pruning method, the synaptic connections in SNNs
can be optimized dynamically to reach an optimal state. As a result, the
structure of SNNs could not only absorb knowledge from the teacher model but
also search for deep but sparse network topology. Experimental results on
CIFAR100 and DVS-Gesture show that the proposed structure learning method can
get pretty well performance while reducing the connection redundancy. The
proposed method explores a novel dynamical way for structure learning from
scratch in SNNs which could build a bridge to close the gap between deep
learning and bio-inspired neural dynamics.
