Abstract
The process of memory and learning in biological systems is multimodal, as several kinds of input signals cooperatively determine the weight of information transfer and storage. This study describes a peptide-based platform of materials and devices that can control the coupled conduction of protons and electrons and thus create distinct regions of synapse-like performance depending on the proton activity. We utilized tyrosine-rich peptide-based films and generalized our principles by demonstrating both memristor and synaptic devices. Interestingly, even memristive behavior can be controlled by both voltage and humidity inputs, learning and forgetting process in the device can be initiated and terminated by protons alone in peptide films. We believe that this work can help to understand the mechanism of biological memory and lay a foundation to realize a brain-like device based on ions and electrons.
Highlights
The process of memory and learning in biological systems is multimodal, as several kinds of input signals cooperatively determine the weight of information transfer and storage
The proton-coupled mechanism that we focus on is ubiquitous in biological systems, as represented by proton-coupled electron transfer (PCET)[3,4]
Through the measurement of the Onsager coefficient to understand the coupling of electrons and protons, it became evident that tyrosine can be involved in PCET and metal ion redox even in the thick films[19]
Summary
The process of memory and learning in biological systems is multimodal, as several kinds of input signals cooperatively determine the weight of information transfer and storage. This study describes a peptide-based platform of materials and devices that can control the coupled conduction of protons and electrons and create distinct regions of synapse-like performance depending on the proton activity. In a neuronal synapse, many chemicals such as neurotransmitters (e.g., dopamine), calcium ions, and protons cooperate in the process of electrical signal transfer, which, interestingly, depends on the external factors determined by sensory input or the memory of the previous events[1,2]. Transient changes in extracellular proton concentrations are important in the activation of further signaling[7,8] In this regard, along with the dopamine-based reward mechanism, proton-based processes can be applied to novel algorithms or synaptic devices. We are further able to control the proton-controlled memory process and the timescale of learning and forgetting, which have never been realized with other materials
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