Memristor-based neural circuits

Biological neural systems use self- reconfigurable and self-learning primitive elements (synapses) to extract relevant information from complex and noisy environments, to detect specific spatio-temporal patterns in the data of interest and to compute and simultaneously store some significant features. All these desirable attributes may be realized by using two-terminal elements, memristors (memory resistors), which most closely resemble biological synapses. This article is organized according to the rule of the ISCAS2013 special session having the same title. We present a short summary of the state-of-the-art of memristor theory and Hodgkin-Huxley neural model. In addition, we briefly introduce a comprehensive nonlinear circuit-theoretic foundation for a novel circuit implementation of the Hodgkin-Huxley neural model with memristors.