The main goal of the field of neuromorphic computing is to build machines that emulate aspects of the brain in its ability to perform complex tasks in parallel and with great energy efficiency. Thanks to new computing architectures, these machines could revolutionize high-performance computing and find applications to perform local, low-energy computing for sensors and robots. The use of organic and soft materials in neuromorphic computing is appealing in many respects, for instance, because it allows better integration with living matter to seamlessly meld sensing with signal processing, and ultimately, stimulation in a closed-feedback loop. Indeed, not only can the mechanical properties of organic materials match those of tissue, but also, the working mechanisms of these devices involving ions, in addition to electrons, are compatible with human physiology. Another advantage of organic materials is the potential to introduce novel fabrication techniques relying on additive manufacturing amenable to one-of-a-kind form factors. This field is still nascent, therefore many concepts are still being proposed, without a clear winner. Furthermore, the field of application of organic neuromorphics, where bioinspiration and biointegration are extremely appealing, calls for a co-design approach from materials to systems.

Many of the current artificial intelligence (AI) applications that are rapidly becoming indispensable in our society rely on software-based artificial neural networks or deep learning algorithms that are powerful, but energy-inefficient. The brain in comparison is highly efficient at similar classification and pattern finding tasks. Neuromorphic engineering attempts to take advantage of the efficiency of the brain by mimicking several crucial concepts to efficiently emulate AI tasks. Organic electronic materials have been particularly successful in mimicking both the basic functionality of the brain, including important spiking phenomena, but also in low-power operation of hardware-implemented artificial neural networks as well as interfacing with physiological environments due to their biocompatible nature. This article provides an overview of the basic functional operation of the brain and its artificial counterparts, with a particular focus on organic materials and devices. We highlight efforts to mimic brain functions such as spatiotemporal processing, homeostasis, and functional connectivity and emphasize current challenges for efficient neuromorphic computing applications. Finally, we present our view of future directions in this exciting and rapidly growing field of organic neuromorphic devices.

Hybrid organic–inorganic halide perovskites have been recently explored as memristive devices that can be programmed to two or more stable conductance states for analog computing. The wide variety and range of optoelectronic phenomena these materials portray offer immense potential to develop scaled-in neuromorphic devices and architectures with multibit memory storage and multimodal accessibility. This article provides a general summary of the structural and optoelectronic characteristics of this material class that could be utilized for neuromorphic computing, discusses insights into the underlying switching mechanisms, and reviews recent developments in the field of halide perovskite-based neuromorphic devices.

We show the design and simulation of organic neuromorphic circuits in a hybrid-computation approach that emulates Boolean and reversible logic gates based on multigate organic electrochemical transistors (OECTs). The organic neuromorphic circuits consist of input, hidden, and output layers that can carry out Boolean operations, including the Exclusive OR (XOR) function, with five or less OECTs. The multigate functionality of OECTs is harnessed to perform the summation function of the neurons. Connection weights of the networks are defined in an unconventional way that depends on the value of the drain-source current of the outputting neuron, which changes according to the input values of the circuit. The Boolean circuits can be cascaded together to build higher level circuits and are demonstrated to form a full adder circuit and the Double Feynman and Toffoli reversible logic gates. Using realistic experimental parameters, the energy per computation is estimated to be ~2.3 nJ for circuit designs with a bias voltage of 0.5 V, with ~230 fJ or less being achievable for lower bias voltages.

Implantable neural interfaces are important tools to accelerate neuroscience research and translate clinical neurotechnologies. The promise of a bidirectional communication link between the nervous system of humans and computers is compelling, yet important materials challenges must be first addressed to improve the reliability of implantable neural interfaces. This perspective highlights recent progress and challenges related to arguably two of the most common failure modes for implantable neural interfaces: (1) compromised barrier layers and packaging leading to failure of electronic components; (2) encapsulation and rejection of the implant due to injurious tissue–biomaterials interactions, which erode the quality and bandwidth of signals across the biology–technology interface. Innovative materials and device design concepts could address these failure modes to improve device performance and broaden the translational prospects of neural interfaces. A brief overview of contemporary neural interfaces is presented and followed by recent progress in chemistry, materials, and fabrication techniques to improve in vivo reliability, including novel barrier materials and harmonizing the various incongruences of the tissue–device interface. Challenges and opportunities related to the clinical translation of neural interfaces are also discussed.

Fundamental materials properties are determined by electrons under the potential energy from the nuclei, the electron mass, and their mutual repulsion. The variable from material to material is the ion potential. The logical procedure of computing electronic properties is to go from the potential to the electron distribution. This enables practical computation of the material properties ranging from atoms and molecules to solids. This method has blossomed due to the effort of numerous people. The concept is analogous to changing prediction of human population distribution from the landscape of hills and dales to determination of the landscape from a population distribution. In atomic systems, quantum quirkiness allows this switch, but dictates that it is only one slice in the tomography of the quantum state. The author shares his experience in the development from this slice, but hews close to the powerful concept of switching the landscape with the population.