Organic electrochemical transistors (OECTs) are an emerging class of devices which operate in electrolytic solution and show controllable memory effects. For these reasons, OECT hold great potential for applications in bioelectronics and neuromorphic computing. Among the methods proposed to fabricate OECT channels, electropolymerization stands out because it allows to produce electrical connections on the substrates on‐demand and further modify them to adjust their electrical properties to meet circuit requirements. However, the practical application of this method is hampered by the difficulty in controlling the growth direction as well as the morphology of the film, resulting in a large device‐to‐device variability and limiting the down‐scaling of the devices. In this study, AC‐electropolymerization is proposed to produce directionally controlled channels. The method allows to adjust physical properties such as resistance and capacitance by varying the polymerization parameters, such as voltage, frequency, and salt concentration. The growth mechanism, material morphology, and network topology is investigated, and the advantages of this approach by showing tunable neuromorphic features and the possibility to scale down the channels to the micrometer scale is demonstrated.

This manuscript serves a specific purpose: to give readers from fields such as material science, chemistry, or electronics an overview of implementing a reservoir computing (RC) experiment with her/his material system. Introductory literature on the topic is rare and the vast majority of reviews puts forth the basics of RC taking for granted concepts that may be nontrivial to someone unfamiliar with the machine learning field. This is unfortunate considering the large pool of material systems that show nonlinear behavior and short-term memory that may be harnessed to design novel computational paradigms. RC offers a framework for computing with material systems that circumvents typical problems that arise when implementing traditional, fully fledged feedforward neural networks on hardware, such as minimal device-to-device variability and control over each unit/neuron and connection. Instead, one can use a random, untrained reservoir where only the output layer is optimized, for example, with linear regression. In the following, we will highlight the potential of RC for hardware-based neural networks, the advantages over more traditional approaches, and the obstacles to overcome for their implementation. Preparing a high-dimensional nonlinear system as a well-performing reservoir for a specific task is not as easy as it seems at first sight. We hope this tutorial will lower the barrier for scientists attempting to exploit their nonlinear systems for computational tasks typically carried out in the fields of machine learning and artificial intelligence. A simulation tool to accompany this paper is available online.

Revealing the intricate logic of neuronal circuits and its connection to the physiopathology of living systems constitutes a fundamental question in neuroscience. Optogenetics offers the possibility to use light of specific wavelengths to study the activity of neurons with unprecedented spatiotemporal resolution. To make use of this technique at its full potential, bidirectional proteins may be expressed across the neuronal membrane to provoke both enhancement and inhibition of neuronal activity depending on the excitation wavelength. This generates the demand for light sources with high spatial precision, high operation speed, and multi‐color emission from the same location. To meet these requirements, the design, realization, and characterization of organic light‐emitting diodes (OLEDs) are presented with switchable bicolor emission, exhibiting high irradiance and good efficiency. The OLEDs can switch between blue and red/green light upon changing the voltage polarity, triggering both optogenetic inhibition and excitation in ND7/23 cells and Drosophila melanogaster larvae expressing bidirectional optogenetic proteins. This work shows the potential of engineering OLEDs to enable multicolor optogenetics with a single, organic device, and provides a new avenue towards bicolor optical brain stimulation in vivo.

Organic electrochemical transistors (OECTs) based on Poly (3,4-ethylenedioxythiophene):poly(styrene sulfonic acid) (PEDOT:PSS) are a benchmark system in organic bioelectronics. In particular, the superior mechanical properties and the ionic-electronic transduction yield excellent potential for the field of implantable or wearable sensing technology. However, depletion-mode operation PEDOT:PSS-based OECTs cause high static power dissipation in electronic circuits, limiting their application in electronic systems. Hence, having control over the threshold voltage is of utmost technological importance. Here we demonstrate PEDOT:PSS-based dual-gate OECTs with solid-state electrolyte where the threshold voltage is seamlessly adjustable during operation. We show that the degree of threshold voltage tuning linearly depends on the gate capacitance, which is a straightforward approach for circuit designers to adjust the threshold voltage only by the device dimensions. The PEDOT:PSS-based dual-gate OECTs show excellent device performance and can be pushed to accumulation-mode operation, resulting in a simplified and relaxed design of complementary inverters.

The application of electrical engineering principles to biology represents the main issue of bioelectronics, focusing on interfacing of electronics with biological systems. In particular, it includes many applications that take advantage of the peculiar optoelectronic and mechanical properties of organic or inorganic semiconductors, from sensing of biomolecules to functional substrates for cellular growth. Among these, technologies for interacting with bioelectrical signals in living systems exploiting the electrical field of biomedical devices have attracted considerable attention. In this review, we present an overview of principal applications of phototransduction for the stimulation of electrogenic and non-electrogenic cells focusing on photovoltaic-based platforms.