A hierarchical model for structure learning based on the physiological characteristics of neurons

A hierarchical model for structure learning based on the physiological characteristics of neurons

WEI Hui

【期刊名称】《中国高等学校学术文摘·计算机科学》 【年(卷),期】2007(001)003

【摘要】Almost all applications of Artificial Neural Networks (ANNs) depend mainly on their memory ability.The characteristics of typical ANN models are fixed connections,with evolved weights,globalized representations,and

globalized

optimizations,all

based

on

a

mathematical approach.This makes those models to be deficient in robustness,efficiency

of

learning,capacity,anti-jamming

between

training sets,and correlativity of samples,etc.In this paper,we attempt to address these problems by adopting the characteristics of biological neurons in morphology and signal processing.A hierarchical neural network was designed and realized to implement structure learning and representations based on connected structures.The basic characteristics of this model are localized and random connections,field limitations of neuron fan-in and fan-out,dynamic behavior of neurons,and samples represented through different sub-circuits of neurons specialized into different response patterns.At the end of this paper,some important aspects of error correction,capacity,learning efficiency,and soundness of structural representation are analyzed theoretically.This paper has