Spike Counts Based Low Complexity Learning with Binary Synapse

Two main difficulties encountered when implementing the neuromorphic system that supports pre- and post-synaptic textbf spike timing based real-time learning, are 1) huge memory size to store synaptic weights and 2) large amount of computations to update the synaptic weights. In this paper, we present a textbf spike counts based learning method that can significantly relieve the hardware burden of the real time unsupervised learning process. The novel learning approach uses both pre- and post-synaptic textbf spike counts as decision metrics in the following two ways: First, the synaptic weights are updated following the proposed simplified mean-based weight update rules, where binary feature images are produced based on pre-synaptic spike counts. Using the feature image, 1 bit synaptic weights are updated only once for each input image. When updating the weights, the post-synaptic spiking counts are also used to select the most active excitatory neuron. The actual weight updates are performed only for the weights connected to the dominant excitatory neuron, leading to further reduction of the weight updates without sacrificing accuracy. The simulation results show that using only 1 bit synaptic weights with 400 output neurons, the proposed learning approach achieves 82 % recognition accuracy with MNIST test set. In addition, the number of weight updates is reduced by 15.3 times compared to the state-of-the-art learning method.