Design of Synaptic Neuronal Circuit Based on Memristors

Abstract To solve the problems of poor learning efficiency and low accuracy caused by the single fixed synaptic weight in the traditional artificial neural network. On the foundation of the improved memristor model, this paper designs a synaptic neuronal circuit based on the natural memory characteristics of the memristor. This synapse is composed of six memristors. The resistance of the memristor is changed by adding a periodic square wave to update the synaptic weight. This circuit can realize signed synaptic weighting, which has certain linear characteristics. Finally, two synaptic weight update methods are proposed based on this circuit, and the validity of the design is verified through Spice simulation experiments.

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Using an artificial neural network for studying the interneuronal layer of a leech neuronal circuit

Biological and Artificial Computation: From Neuroscience to Technology - Lecture Notes in Computer Science ◽

10.1007/bfb0032478 ◽

1997 ◽

pp. 207-216

Author(s):

Juan Miguel Santos ◽

Lidia Szczupak

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Neuronal Circuit ◽

Artificial Neural

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The Backpropagation Artificial Neural Network Based on Elite Particle Swam Optimization Algorithm for Stochastic Linear Bilevel Programming Problem

Mathematical Problems in Engineering ◽

10.1155/2018/1626182 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Tao Zhang ◽

Xiaofei Li

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Programming Problem ◽

Optimization Algorithm ◽

Bilevel Programming ◽

Bilevel Programming Problem ◽

Simulation Experiments ◽

Linear Bilevel Programming ◽

Artificial Neural ◽

Backpropagation Artificial Neural Network

For a class of stochastic linear bilevel programming problem, we firstly transform it into a deterministic linear bilevel covariance programming problem. Then, the deterministic bilevel covariance programming problem is solved by backpropagation artificial neural network based on elite particle swam optimization algorithm (BPANN-PSO). Finally, we perform the simulation experiments and the results show that the computational efficiency of the proposed algorithm has a potential upside compared with the classical algorithm.

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Run-off election-based decision method for the training and inference process in an artificial neural network

Scientific Reports ◽

10.1038/s41598-020-79452-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jingon Jang ◽

Seonghoon Jang ◽

Sanghyeon Choi ◽

Gunuk Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Decision Rule ◽

Synaptic Weight ◽

Activation Function ◽

Network System ◽

Run Off ◽

Neural Network System ◽

Artificial Neural ◽

Fully Connected

AbstractGenerally, the decision rule for classifying unstructured data in an artificial neural network system depends on the sequence results of an activation function determined by vector–matrix multiplication between the input bias signal and the analog synaptic weight quantity of each node in a matrix array. Although a sequence-based decision rule can efficiently extract a common feature in a large data set in a short time, it can occasionally fail to classify similar species because it does not intrinsically consider other quantitative configurations of the activation function that affect the synaptic weight update. In this work, we implemented a simple run-off election-based decision rule via an additional filter evaluation to mitigate the confusion from proximity of output activation functions, enabling the improved training and inference performance of artificial neural network system. Using the filter evaluation selected via the difference among common features of classified images, the recognition accuracy achieved for three types of shoe image data sets reached ~ 82.03%, outperforming the maximum accuracy of ~ 79.23% obtained via the sequence-based decision rule in a fully connected single layer network. This training algorithm with an independent filter can precisely supply the output class in the decision step of the fully connected network.

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