Brain-Inspired Synaptic Resistor Circuits for Self-Programming Intelligent Systems

Unlike artificial intelligent systems based on computers, which need to be preprogrammed for specific tasks, restricting their functions to their preprogrammed ranges, the human brain does not need to be preprogrammed, and has general intelligence to create new tactics in complex and erratic environments. The basic element in the brain, a synapse, has the function to process and learn from signals in real time by following Hebb's rule, which is a critical function missing from the transistor, the basic device in computers. In this work, a computing circuit based on synaptic resistors (synstors) with signal processing and Hebbian learning functions is modeled and analyzed. A synstor circuit emulates a neurobiological network to concurrently execute signal processing and learning algorithms in parallel mode, does not need to be preprogrammed, and has the capability to optimize and create new algorithms in complex and erratic environments with speed and energy efficiency significantly superior to those of existing computing circuits. The synstor circuit can potentially circumvent the fundamental limitations of existing computing circuits, leading to a new computing platform with real-time self-programming functionality and general intelligence in complex and erratic environments.