Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

Abstract

In neural system, both spike-timing-dependent plasticity (STDP) and conditioned reflex are vital synaptic learning mechanisms to regulate advanced neural activities, being associated with temporally coupled stimuli. Thus, realization of STDP and conditioned reflex on a single solid-state device may open up new opportunities for neuromorphic engineering. Here, restickable chitosan-gated oxide neuromorphic transistors are fabricated on polyimide tape. Based on protonic electrochemical doping/de-doping processes at indium-tin-oxide/chitosan interfaces, four types of STDP learning rules are successfully demonstrated, including Hebbian STDP, anti-Hebbian STDP, symmetrical STDP, and visual STDP. Trained with Hebbian STDP, Pavlovian associative learning and extinction behaviors are demonstrated successfully on a single-oxide neuromorphic transistor. No complex device circuits are needed. It is interesting to note here that the devices can be pasted on different holders with different curvature radii without degrading the transistor performances and STDP learning rules. Moreover, the proposed oxide neuromorphic transistors can be dissolved in deionized water easily. The results here indicate potential applications of the proposed restickable oxide neuromorphic transistors in flexible neuromorphic cognitive platforms.

Ionotronic Neuromorphic Devices for Bionic Neural Network Applications

“…The α‐MoO 3 channel conductance can be repeatedly potentiated from 3.5 to 4.75 nA under positive pulses and depressed back to 3.5 nA under negative pulses. Similar potentiation and depression behaviors were also reported in nanogranular SiO 2 and chitosan‐based electrolyte gated oxide neuromorphic transistors . Fu et al also fabricated iontronic neuromorphic transistor based on mixed protonic and electronic conductor hybrid (MPEC) IWO transistor.…”

“…In the past few years, ionotronic transistors or memristors were proposed to mimic advanced neural functions. By connecting pressure sensors and oscilloscope with neuromorphic devices and adoping mathematical modeling and software simulation, advanced neural functions are simulated, including logic function, image memorization, pattern recognition, face recognition, classic conditioning, tactile‐perception system, etc. In this section, we shortly discuss these achievements.…”

“…Thus, there will be the relation between STDP learning rule and classic conditioning. Yu et al proposed a restickable chitosan‐gated ITO neuromorphic transistor. They have mimicked four types of STDP learning rules by designing different synaptic spikes.…”

Near‐Infrared‐Irradiation‐Mediated Synaptic Behavior from Tunable Charge‐Trapping Dynamics

“…[10][11][12][13] Therefore, the emulated synaptic plasticity of STP and LTP render themselves supportive to the sophisticated cognitive function and adaptive behavior pattern.The emphasis on neuroinspired computing so far has been predominantly in electrical stimulation-induced resistance state switching in phase change memories, [14,15] memristors, [16][17][18][19][20][21][22][23][24] as well as transistor-based memories. To achieve artificial synaptic recognition for learning and memory in electronic devices, concomitant plasticity with different timescale is essential.…”

“…[10][11][12][13] Therefore, the emulated synaptic plasticity of STP and LTP render themselves supportive to the sophisticated cognitive function and adaptive behavior pattern. To achieve artificial synaptic recognition for learning and memory in electronic devices, concomitant plasticity with different timescale is essential.…”