LAUSR

Artificial Intelligence (AI) has become very popular due to both the increasing demands from applications and the booming of computer techniques. Spiking Neural Network (SNN), as the third generation of Artificial Neural Network, receives more and more attention in the field of AI. With the high similarity to biological neural network, SNN has the potential to break through the barriers of strong AI. However, the using of SNNs on practical scenarios is rather limited, as a result of the lack of high efficient learning algorithms. Nowadays, learning methods of SNNs are designed mainly based on previous biological discoveries. The fact that there are both excitatory neurons and inhibitory neurons in the biological neural network has stimulated the motive of this research. The existence of inhibitory neurons could strengthen the self-regulation ability of neural networks and improve learning efficiency. Inspired by the ancient Chinese “Yin and Yang” Theory, we first presented our effort at constructing SNN structure with equilibrated excitatory neurons and inhibitory neurons. Then an ensemble learning optimized supervised learning method is designed and tailored for this SNN structure. Experiments are conducted using MNIST data sets, and results show that, with the designed learning mechanism, our equilibrated bipolar SNN structure could gain reasonable accuracy with much more compact structure and much more sparse synapse connections.