  With the continued innovations of deep neural networks, spiking neural
networks (SNNs) that more closely resemble biological brain synapses have
attracted attention owing to their low power consumption.However, for
continuous data values, they must employ a coding process to convert the values
to spike trains.Thus, they have not yet exceeded the performance of artificial
neural networks (ANNs), which handle such values directly.To this end, we
combine an ANN and an SNN to build versatile hybrid neural networks (HNNs) that
improve the concerned performance.To qualify this performance, MNIST and
CIFAR-10 image datasets are used for various classification tasks in which the
training and coding methods changes.In addition, we present simultaneous and
separate methods to train the artificial and spiking layers, considering the
coding methods of each.We find that increasing the number of artificial layers
at the expense of spiking layers improves the HNN performance.For
straightforward datasets such as MNIST, it is easy to achieve the same
performance as ANNs by using duplicate coding and separate learning.However,
for more complex tasks, the use of Gaussian coding and simultaneous learning is
found to improve the accuracy of HNNs while utilizing a smaller number of
artificial layers.
