Our brain is a highly parallel, energy efficient and event-driven information processing system, which is fundamentally different from traditional von Neumann computers. Neuron is the most important building block, and information processing in individual neuron involves the transformation of input synaptic spike train into an appropriate output spike train. Inspired by biological neural computing, neuromorphic systems may open up new paradigms to deal with complicated problems such as pattern recognition, classification and decision making. The idea of building brain-inspired adaptive artificial devices has been around for decades, and hardware implementation of neuron by individual ionic/electronic hybrid device is of great significance for enhancing our understanding of the brain and solving sensory processing and complex recognition tasks. Although two-terminal memristors can performe some basic synaptic and neural functions, our human brain contains many more synapses than neurons. This fact suggests that multi-terminal devices are more favorable for complex neural nwtwork emulation. In recent years, multi-terminal electric-double-layer (EDL) Thin-film transistors (TFTs) based on interfacial ion-modulation have attracted significant attention in mimicking synaptic dynamic plasticity and neural functions. Here, we provide the proof-of-principle artificial synapses/neurons based on solid electrolytes coupled oxide-based EDL TFTs with multiple driving and modulatory inputs terminals. Paired-pulse facilitation, dendritic integration and orientation tuning were successfully emulated. Additionally, neuronal gain control (arithmetic) in the scheme of rate coding is also experimentally demonstrated. Our results provide a new-concept approach for building brain-like cognitive systems.
References
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