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Runtime detection of activated polychronous neuronal group towards its spatiotemporal analysis

: Sun, Haoqi; Yang, Yan; Sourina, O.; Huang, Guang-Bin


Institute of Electrical and Electronics Engineers -IEEE-; International Neural Network Society; IEEE Computational Intelligence Society:
International Joint Conference on Neural Networks, IJCNN 2015. Vol.2 : Killarney, Ireland, 12 - 17 July 2015
Piscataway, NJ: IEEE, 2015
ISBN: 978-1-4799-1961-1
ISBN: 978-1-4799-1959-8
International Joint Conference on Neural Networks (IJCNN) <2015, Killarney>
Conference Paper
Fraunhofer IDM@NTU ()

Due to the precise spike timing in neural coding, spiking neural network (SNN) possesses richer spatiotemporal dynamics compared to neural networks with firing rate coding. One of the distinct features of SNN, polychronous neuronal group (PNG), receives much attention from both computational neuroscience and machine learning communities. However, all existing algorithms detect PNGs from the spike recording collected after simulation in an offline manner. There is currently no algorithm that detects PNGs actually being activated in runtime (online manner), which could be potentially used as inputs to higher level neural processing. We propose a runtime detection algorithm particularly for activated PNGs, using PNG readout neurons, to fill this gap. The proposed algorithm can reveal the spatiotemporal PNG patterns embedded in spike trains, which is higher level neuronal dynamics. We demonstrate through an example that for composed input patterns, new PNGs except the constituent PNGs can be easily found using the proposed algorithm. As an important interpretation, we give further insights on how to use PNG readout neurons to construct layered network structure.