Artificial intelligence applications have demonstrated their enormous potential for complex processing over the last decade. However, they are mainly based on digital operating principles while being part of an analogue world. Moreover, they still lack the efficiency and computing capacity of biological systems. Neuromorphic electronics emulate the analogue information processing of biological nervous systems. Neuromorphic electronics based on organic materials have the ability to emulate efficiently and with fidelity a wide range of bio-inspired functions. A prominent example of a neuromorphic device is based on organic mixed conductors (ionic-electronic). Neuromorphic devices based on organic mixed conductors show volatile, non-volatile and tunable dynamics suitable for the emulation of synaptic plasticity and neuronal functions, and for the mapping of artificial neural networks in physical circuits. Finally, small-scale organic neuromorphic circuits enable the local sensorimotor control and learning in robotics.
The seamless integration of electronics with biology requires new bio-inspired approaches that, analogously to nature, rely on the presence of electrolytes for signal multiplexing. On the contrary, conventional multiplexing schemes mostly rely on electronic carriers and require peripheral circuitry for their implementation, which imposes limitations toward their adoption in bio-applications. Here we show an iontronic multiplexer based on spatiotemporal dynamics of organic electrochemical transistors (OECTs), with an electrolyte as the shared medium of communication. The iontronic system discriminates locally random-access events with no need of peripheral circuitry, thus deceasing significantly the integration complexity. The form factors of OETCs, open new avenues for unconventional multiplexing in the emerging fields of bioelectronics and neuromorphic sensors. Examples of organic neuromorphic electronics for local learning in applications with energy restrictions are also showcased.
Ions are fundamental biological regulators enabling the communication between cells, regulating metabolic and bioenergetic processing and playing a key role in pH regulation and hydration. The in-situ quantification of the ion concentration is gathering relevant interest in biomedical diagnostics and healthcare. State-of-art transistor-based ion sensors show an intrinsic trade-off between sensitivity, operating range and supply voltage. To overcome these limitations, here we focus on ion sensor amplifiers where complementary OECTs are integrated in a push-pull configuration, providing sensitivity larger than 1 V/dec at a supply voltage down to 0.5 V and operating in the physiological range. Ion detection over a range of five orders of magnitude and real-time monitoring of variations two orders of magnitude lower than the detected concentration are achieved. The ion-sensitive amplifier sets a new benchmark for ion-sensing devices, opening possibilities for predictive diagnostics and personalized medicine.
It is now well recognized that traditional computing systems based on von Neumann architecture are not efficient enough to manipulate and process the massive amount of data produced by the contemporary information technologies. A shifting paradigm from the traditional computing systems is the emulation of the brain computational efficiency at the hardware-based level, a field that is also known as neuromorphic computing. Although neuromorphic computing with inorganic materials has been advanced over the past years, nevertheless biological plausibility is questionable in many cases of solid-state technologies. In the brain, for instance, neural populations are immersed in a common electrolyte or cerebrospinal fluid and this fact equips the brain with more efficient features in processing when compared to electronic devices or circuits. Due to this topology in biological neural networks, higher order phenomena exist such as global regulation of neural activity and communication between different regions in the brain mediated by the presence of the global electrolyte. In this work, device concepts will be presented that lead to biological plausibility in organic neuromorphic devices, including global phenomena and synchronization functions. Introducing this level of biological plausibility, paves the way for new concepts of neuromorphic communication between different subunits in a circuit.
Neuromorphic devices and architectures offer novel ways of data manipulation and processing, especially in data intensive applications. At a single device level, various forms of neuroplasticity have been emulated over the past years, mainly with inorganic devices. The implementation of neuroplasticity functions with these devices also enabled applications at a circuit level related to machine learning such as feature or pattern recognition. Although the field of organic-based neuromorphic devices and circuits is still at its infancy, organic materials may offer attractive features for neuromorphic engineering. Over the past years for example, a few simple neuromorphic functions have been demonstrated with biological substances and bioelectronic devices. In this work various neuromorphic devices will be presented that are based on organic mixed conductors, materials that are traditionally used in organic bioelectronics. A prominent example of a device in bioelectronics that exploits mixed conductivity phenomena is the organic electrochemical transistor (OECT). Devices based on OECTs show volatile and tunable dynamics suitable for the emulation of short-term synaptic plasticity functions. Chemical synthesis allows for the introduction of non-volatile phenomena suitable for long-term memory functions. The device operation in common electrolyte permits the definition of spatially distributed multiple inputs at a single device level. The presence of a global electrolyte in an array of devices also allows for the homeostatic or global control of the array. Global electrical oscillations can be used as global clocks that frequency-lock the local activity of individual devices in analogy to the global oscillations in the brain. Finally, “soft” interconnectivity through the electrolyte can be defined, a feature that paves the way for parallel interconnections between devices with minimal hard-wired connections.
Neuroinspired device architectures offer the potential of higher order functionalities in information processing beyond their traditional microelectronic counterparts. In the actual neural environment, neural processing takes place in a complex and interwoven network of neurons and synapses. In addition, this network is immersed in a common electrochemical environment and global parameters such as ionic concentrations and concentrations of various hormones regulate the overall behaviour of the network. Here, various concepts of organic neuromorphic devices are presented based on organic electrochemical transistors (OECTs). Regarding the implementation of neuromorphic devices, the key properties of the OECT that resemble the neural environment are also presented. These include the operation in liquid electrolyte environment, low power consumption and the ability of formation of massive interconnections through the electrolyte continuum. Showcase examples of neuromorphic functions with OECTs are demonstrated, including short-, long-term plasticity and spatiotemporal or distributed information processing.
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