Modular neuron comprises of memristor-based synapse. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Shamsi, Jafar; Amirsoleimani, Amirali; Mirzakuchaki, Sattar; Ahmadi, Majid
• Is Part Of: Neural computing & applications. Volume 28:Issue 1(2017)
• Page Start: 1
• Page End: 11
• Publication Date: 2017-01
• Subjects: Artificial neural network -- Synaptic operation -- Distributed neuron -- Memristor bridge synapse; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2047-0
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Design of analog modular neuron based on memristor is proposed here. Since neural networks are built by repetition of basic blocks that are called neurons, using modular neurons is essential for the neural network hardware. In this work modularity of the neuron is achieved through distributed neurons structure. Some major challenges in implementation of synaptic operation are weight programmability, weight multiplication by input signal and nonvolatile weight storage. Introduction of memristor bridge synapse addresses all of these challenges. The proposed neuron is a modular neuron based on distributed neuron structure which it uses the benefits of the memristor bridge synapse for synaptic operations. In order to test appropriate operation of the proposed neuron, it is used in a real-world application of neural network. Off-chip method is used to train the neural network. The results show 86.7 % correct classification and about 0.0695 mean square error for 4-5-3 neural network based on proposed modular neuron.