Research on artificial neural networks with spatial architecture based on span connection and lateral inhibition mechanism Online publication date: Wed, 18-Mar-2015
by Gang Yang, Jun-Fei Qiao, Ying-Chun Bo
International Journal of Computational Science and Engineering (IJCSE), Vol. 6, No. 1/2, 2011
Abstract: Motivated by the knowledge of biological neural system being an asymmetry three dimensional structure and the effect of inhibition within cerebral cortex, we propose a novel topology of artificial neural network called spatial artificial neural network (SANN), which includes two types of processing networks: basic network and spatial connection network. The basic network introduces the lateral inhibition mechanism between hidden units and realises the competition in neurons. The spatial connection means that any two neurons in SANN may have random and long-range connectivity. Supervised learning rules for synaptic weights update are derived from the steepest descent gradient, and the descent gradient with momentum (GDM) is used for network learning. From the experimental analysis of benchmark problems such as pattern recognition, non-linear function approximation, we prove the powerful representation capability and generalisation performance of SANN network.
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