Information processing in the brain takes place in a network of neurons that are connected with each other by an immense number of synapses. At the same time, neurons are immersed in a common electrochemical environment, and global parameters such as concentrations of various hormones regulate the overall network function. This computational paradigm of global regulation, also known as homeoplasticity, has important implications in the overall behaviour of large neural ensembles and is barely addressed in neuromorphic device architectures. Here, we demonstrate the global control of an array of organic devices based on poly(3,4ethylenedioxythiophene):poly(styrene sulf) that are immersed in an electrolyte, a behaviour that resembles homeoplasticity phenomena of the neural environment. We use this effect to produce behaviour that is reminiscent of the coupling between local activity and global oscillations in the biological neural networks. We further show that the electrolyte establishes complex connections between individual devices, and leverage these connections to implement coincidence detection. These results demonstrate that electrolyte gating offers significant advantages for the realization of networks of neuromorphic devices of higher complexity and with minimal hardwired connectivity.

Functional emulation of biological synapses using electronic devices is regarded as the first step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte-gated transistors (EGTs) are mixed ionic–electronic conductivity devices capable of efficient gate-channel capacitance coupling, biocompatibility, and flexible architectures. Electrolyte gating offers significant advantages for the realization of neuromorphic devices/architectures, including ultralow-voltage operation and the ability to form parallel-interconnected networks with minimal hardwired connectivity. In this review, the most recent developments in EGT-based electronics are introduced with their synaptic behaviors and detailed mechanisms, including short-/long-term plasticity, global regulation phenomena, lateral coupling between device terminals, and spatiotemporal correlated functions. Analog memory phenomena allow for the implementation of perceptron-based ANNs. Due to their mixed-conductivity phenomena, neuromorphic circuits based on EGTs allow for facile interfacing with biological environments. We also discuss the future challenges in implementing low power, high speed, and reliable neuromorphic computing for large-scale ANNs with these neuromorphic devices. The advancement of neuromorphic devices that rely on EGTs highlights the importance of this field for neuromorphic computing and for novel healthcare technologies in the form of adaptable or trainable biointerfacing.

The effective mimicry of neurons is key to the development of neuromorphic electronics. However, artificial neurons are not typically capable of operating in biological environments, which limits their ability to interface with biological components and to offer realistic neuronal emulation. Organic artificial neurons based on conventional circuit oscillators have been created, but they require many elements for their implementation. Here we report an organic artificial neuron that is based on a compact nonlinear electrochemical element. The artificial neuron can operate in a liquid and is sensitive to the concentration of biological species (such as dopamine or ions) in its surroundings. The system offers in situ operation and spiking behaviour in biologically relevant environments—including typical physiological and pathological concentration ranges (5–150 mM)—and with ion specificity. Small-amplitude (1–150 mV) electrochemical oscillations and noise in the electrolytic medium shape the neuronal dynamics, whereas changes in ionic (≥2% over the physiological baseline) and biomolecular (≥ 0.1 mM dopamine) concentrations modulate the neuronal excitability. We also create biohybrid interfaces in which an artificial neuron functions synergistically and in real time with epithelial cell biological membranes.

Synaptic plasticity functions play a crucial role in the transmission of neural signals in the brain. Short-term plasticity is required for the transmission, encoding, and filtering of the neural signal, whereas long-term plasticity establishes more permanent changes in neural microcircuitry and thus underlies memory and learning. The realization of bioinspired circuits that can actually mimic signal processing in the brain demands the reproduction of both short- and long-term aspects of synaptic plasticity in a single device. Here, we demonstrate the implementation of neuromorphic functions similar to biological memory, such as short- to long-term memory transition, in non-volatile organic electrochemical transistors (OECTs). Depending on the training of the OECT, the device displays either short- or long-term plasticity, therefore, exhibiting non von Neumann characteristics with merged processing and storing functionalities. These results are a first step towards the implementation of organic-based neuromorphic circuits.