Title: A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule,
Abstract: Spiking neural networks (SNNs) possess energy-efficient potential due to
event-based computation. However, supervised training of SNNs remains a
challenge as spike activities are non-differentiable. Previous SNNs training
methods can basically be categorized into two classes, backpropagation-like
training methods and plasticity-based learning methods. The former methods are
dependent on energy-inefficient real-valued computation and non-local
transmission, as also required in artificial neural networks (ANNs), while the
latter either be considered biologically implausible or exhibit poor
performance. Hence, biologically plausible (bio-plausible) high-performance
supervised learning (SL) methods for SNNs remain deficient. In this paper, we
proposed a novel bio-plausible SNN model for SL based on the symmetric
spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By
combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic
plasticity of the dynamic threshold, our SNN model implemented SL well and
achieved good performance in the benchmark recognition task (MNIST). To reveal
the underlying mechanism of our SL model, we visualized both layer-based
activities and synaptic weights using the t-distributed stochastic neighbor
embedding (t-SNE) method after training and found that they were well
clustered, thereby demonstrating excellent classification ability. As the
learning rules were bio-plausible and based purely on local spike events, our
model could be easily applied to neuromorphic hardware for online training and
may be helpful for understanding SL information processing at the synaptic
level in biological neural systems.