Research On Realization Of Neuron Circuits

Artificial neural network is an important method in the field of modern information processing.Comparing to software realization method,hardware realization method can fully exert the characteristics of parallel work of neural network.Neural networks realized by analog circuits have simpler structure,lower power consumption and higher speed and occupy small chip area than by digital circuits,but digital circuit is more easy storage information.So neuron circuit Realized by analog/digital mixed circuit is an ideal method.By analysing the principle of nerton,a method was developed for designing neuron circuits in this paper.Mainly proceeding from the mission of reducing neuron numbers in neural networks(NNS)and enhancing the information proceeing abilities of single neuron,as well focusing on the digital logic implementation by multi-thresholded neurons(MTNS),this dissertation makes it possible that digital logic can be implemented by a small mumber of neurons and the NNS realize digital logic may have high information density.First analyzing the principle of multi-thresholded neurons(MTNs),give the designing multi-thresholded neuron circuits(MTNCs).First,the voltage-mode synapse circuits wan designed using by two MOS transistors.Then based on a verdict-converting switch(VCS),an approach was propose for designing the circuits of multi-thresholded verdict function(MTVF) at switch level.Several MTNCs were designed for implementing the literal,AND,NOT operation as three operations in ternary logic at switch level.The result of simulation with PSPICE showed that the designed circuits had the correct logic function with simple structure,what's more,when achieving the same logic function,The MTNCs decreased large amount of hardware expense compared with the circuits.Finally the paper give a method was developed for designing the synapse circuits of digital weight-storage base on two MOS transistors.Building the unified model for multi-thresholded neurons in Multi-valued operations.