Spike timing-dependent conduction delay learning model classifying spatio-temporal spike patterns

Precise spike timing is considered to play a fundamental role in communication and signal processing in biological neural networks. Understanding such mechanism contributes to both deep understanding of biological system and development of engineering applications such as efficient computational architectures. However, the biological mechanism which adjusts and maintains the spike timing still remains unclear. Previous studies have proposed algorithms adjusting synaptic efficacy and axonal conduction delay so that the spike timings get close to the desired spike timings in supervised manner. Supervised learning always requires desired spike timings as teacher signal, and thus it should not be dominant in biological system, which is considered to adapt to environment without teacher. This study proposes a spike timing-dependent learning model adjusting synaptic efficacy and axonal conduction delay in both unsupervised and supervised manners. The proposed learning algorithm approximates Expectation-Maximization algorithm and can classify the input data coded into spatio-temporal spike patterns. Furthermore, the proposed learning algorithm agrees with various results of existing biological experiments such as spike timing-dependent plasticity, and therefore it could be a good candidate of a model of biological delay learning.