DOI: https://doi.org/10.29363/nanoge.DEPERO.2023.004
Publication date: 14th September 2023
In recent years, cutting-edge research has been conducted worldwide on advanced neuroelectronic materials with synaptic properties that have driven the development of brain-inspired computation [1]. One of the central functions in the human brain is the biological synapsis, which has the essential property of long-term potentiation, where certain patterns of incoming stimuli modify the conductivity of the elements, favoring or inhibiting current flow. This essential phenomena of learning, memory and inference in the cognitive human brain is indispensable for the real-time information-processing and transmission. Thus, the analysis of materials with memory effects is indeed necessary to reproduce the natural functions of potentiation. Nevertheless, the fundamental understanding of the nonlinear relaxation processes to repetitive stimulus is not fully understood yet and cannot be related to basic materials properties.
Here, we study the evolution of the time transient responses that constitute the long-term potentiation mechanism of the popular halide perovskite memristors inspired in artificial neurons [2,3]. We identify the bioelectrical origin of the system’s response in terms of capacitive, inductive, and resistive elements, that can be put in correspondence with the classical ionic-electronic dynamics of perovskite semiconductors. From an advanced mathematical model based on a few nonlinear equations that is extremely successful to reproduce the multiple faces of transient responses in the long-term potentiation, we draw a physical picture that facilitates a sound dynamic interpretation of this important process of the synaptic operation [4]. This new methodology is a very powerful tool for the analysis of different types of synaptic responses and, more generally, for electronic devices with memory effects.
This work has received funding from the Universidad Rey Juan Carlos (project of reference M2993). This study forms part of the Advanced Materials program and was supported by MCIN with funding from the European Union NextGenerationEU (PRTR-C17. I1) and Generalitat Valenciana.
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