2016/04/25 by Akshay Kumar Maan, Deepthi Anirudhan Jayadevi, Alex Pappachen James
Computer Science · Engineering · Mathematics · Neuroscience · #Advanced Memory and Neural Computing #Algorithm #Artificial intelligence #Artificial neural network #Computer architecture #Computer hardware #Computer science #Control logic #Electrical engineering #Electronic circuit #Electronic engineering #Engineering #Logic gate #Mathematics #Memristor #Neuromorphic engineering #Neuroscience and Neural Engineering #Photoreceptor and optogenetics research #Realization (probability) #Transistor #Voltage #cs.ET
paper · pdf · doi:10.1109/tnnls.2016.2547842
published as IEEE Transactions on Neural Networks and Learning Systems, 2016
arxiv created 2016/04/25 · arxiv updated 2016/04/26 · openalex publication_date 2016/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore's law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing, control systems, programmable logic, image processing, reconfigurable computing, and pattern recognition are identified as some of the potential applications of MTL systems. The physical realization of nanoscale devices with memristive behavior from materials, such as TiO2, ferroelectrics, silicon, and polymers, has accelerated research effort in these application areas, inspiring the scientific community to pursue the design of high-speed, low-cost, low-power, and high-density neuromorphic architectures.