PEDOT:PSS Wire: A Two-Terminal Synaptic Device for Operation in Electrolyte and Saline Solutions.

Synaptic devices, which are designed to emulate the synaptic functions of neurons, have recently gained attention as key elements in the development of neuromorphic hardware. To date, most synaptic devices utilizing conductive polymer materials, particularly poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS), have been designed as three-terminal devices. Nevertheless, a recent study revealed that a single PEDOT:PSS wire can function as a two-terminal synaptic device through additional polymerization, which creates asymmetry in the wire diameter between the anode and cathode. Owing to its high biocompatibility, PEDOT is considered a promising candidate for use in clinical information-processing devices. However, previous studies examined the synaptic function of PEDOT:PSS only in PSS solutions. Therefore, the performance of PEDOT:PSS wires in other solutions, such as physiological saline solutions, remains unknown. Herein, we investigated the effects of operating environmental conditions (including phosphate-buffered saline (PBS)) on the synaptic functions of the asymmetric PEDOT:PSS wire. Our results indicate that the synaptic conductance change in the PEDOT:PSS wire occurred in all investigated aqueous electrolyte solutions. Moreover, we revealed the relationship between the synaptic conductance change behavior and the molecular properties of the electrolyte ions. Furthermore, the waveform of the conductance change can be controlled by adjusting the conditions for wire asymmetrization. These results demonstrate that the PEDOT:PSS wire exhibits a synaptic conductance change, yielding a waveform suitable for machine learning, even under wet conditions (i.e., in any electrolyte solution, including PBS). Therefore, PEDOT:PSS wire is a promising material for two-terminal synaptic devices applicable in clinical studies.

Highly-integrable analogue reservoir circuits based on a simple cycle architecture.

Physical reservoir computing is a promising solution for accelerating artificial intelligence (AI) computations. Various physical systems that exhibit nonlinear and fading-memory properties have been proposed as physical reservoirs. Highly-integrable physical reservoirs, particularly for edge AI computing, has a strong demand. However, realizing a practical physical reservoir with high performance and integrability remains challenging. Herein, we present an analogue circuit reservoir with a simple cycle architecture suitable for complementary metal-oxide-semiconductor (CMOS) chip integration. In several benchmarks and demonstrations using synthetic and real-world data, our developed hardware prototype and its simulator exhibit a high prediction performance and sufficient memory capacity for practical applications, showing promise for future applications in highly integrated AI accelerators.

Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses.

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.