Owing to its close resemblance to biological systems and materials, soft matter has been
successfully implemented in numerous bioelectronic and biosensing applications, as well as …

Neuromorphic computing could address the inherent limitations of conventional silicon
technology in dedicated machine learning applications. Recent work on silicon-based …

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional
computing through parallel programming and readout of artificial neural network weights in …

Brain-inspired computing paradigms have led to substantial advances in the automation of
visual and linguistic tasks by emulating the distributed information processing of biological …

Organic mixed ionic-electronic conductors (OMIECs) directly convert between ionic and
electronic charge through electrochemical (de) doping, enabling a wide range of …

Devices with tunable resistance are highly sought after for neuromorphic computing.
Conventional resistive memories, however, suffer from nonlinear and asymmetric resistance …

Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are
promising to process spatiotemporal information and can provide highly time‐and energy …

The von Neumann computing architecture consists of separated processing and memory
elements; it is too bulky and energy-intensive to be implemented in the upcoming artificial …

Functional emulation of biological synapses using electronic devices is regarded as the first
step toward neuromorphic engineering and artificial neural networks (ANNs). Electrolyte …

Electronic transport models for conducting polymers (CPs) and blends focus on the
arrangement of conjugated chains, while the contributions of the nominally insulating …