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The recent success of Deep Neural Networks (DNNs) has revealed thesignificant capability of neural computing in many challenging applications.Although DNNs are derived from emulating biological neurons, there still existdoubts over whether or not DNNs are the final and best model to emulate themechanism of human intelligence. In particular, there are two discrepanciesbetween computational DNN models and the observed facts of biological neurons.First, human neurons are interconnected randomly, while DNNs needcarefully-designed architectures to work properly. Second, human neuronsusually have a long spiking latency (~100ms) which implies that not many layerscan be involved in making a decision, while DNNs could have hundreds of layersto guarantee high accuracy. In this paper, we propose a new computationalmodel, namely shallow unorganized neural networks (SUNNs), in contrast toANNs/DNNs. The proposed SUNNs differ from standard ANNs or DNNs in threefundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, SmartNeurons, that allows each artificial neuron cell to adaptively respond to itsinputs rather than carrying out a fixed weighted-sum operation like the classicneuron model in ANNs/DNNs; 2) SUNNs can cope with computational tasks with veryshallow architectures; 3) SUNNs have a natural topology with randominterconnections, as the human brain does, and as proposed by Turing's B-typeunorganized machines. We implemented the proposed SUNN architecture and testedit on a number of unsupervised early stage visual perception tasks.Surprisingly, such simple shallow architectures achieved very good results inour experiments. The success of our new computational model makes it the firstworkable example of Turing's B-Type unorganized machine that can achievecomparable or better performance against the state-of-the-art algorithms.

Shallow Unorganized Neural Networks using Smart Neuron Model for Visual Perception