Proceedings of MATSUS Fall 2023 Conference (MATSUSFall23)
DOI: https://doi.org/10.29363/nanoge.matsus.2023.081
Publication date: 18th July 2023
The replication of neural information processing in electrical devices has been extensively studied over the years. The paradigm of parallel computing, which allows information to be simultaneously detected, processed and stored, is required for numerous applications in many fields. In the case of brain-computer interfaces, another important requirement is the suitability of the device for communication with cells. Organic electrochemical transistors (OECTs) based on PEDOT:PSS are used for this purpose due to their ionic-to-electronic signal transduction and biocompatibility [1]. Many works have demonstrated the reproduction of neural plasticity mechanisms, such as short-term facilitation and long-term potentiation. In each device, the physical mechanism of transduction may be different, but it is known that the electrolyte plays a key role in the functioning of these devices, as it provides the ions responsible for the chemical transmission of information. Focusing on long-term memory, this can be reproduced in the OECTs with the oxidation of the neurotransmitter, as in the case of the biohybrid synapse [2]. It is crucial to understand the influence of the material chemistry and the electrolyte composition on the memory effect of the device, as long-term modulation is based on a change in the ionic balance between the electrolyte and the organic polymer.
This electrolyte-dependency plasticity will be discussed as it should be considered when the OECT is used in a biological environment in which a large number of molecules of a different nature are present in addition to neurotransmitters. Furthermore, I will discuss how conjugated polymers can be engineered with azopolymers (opto-sensitive polymers which switch from cis to trans conformation upon certain light exposure) to feature diverse optoelectronic short and long term plasticity, enabling the use of such platforms as neurohybrid devices as building blocks of retina-inspired devices.
References
1 Bernard et al. (2007). In: Advanced Functional Materials 17.17, pp. 3538–3544.
2 Keene, Scott T et al. (2020). In: Nature Materials 19.9, pp. 969–973
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