The integration and cooperation of mechanoreceptors, neurons and synapses in somatosensory systems enable humans to efficiently sense and process tactile information. Inspired by biological somatosensory systems, we report an optoelectronic spiking afferent nerve with neural coding, perceptual learning and memorizing capabilities to mimic tactile sensing and processing. Our system senses pressure by MXene-based sensors, converts pressure information to light pulses by coupling light-emitting diodes to analog-to-digital circuits, then integrates light pulses using a synaptic photomemristor. With neural coding, our spiking nerve is capable of not only detecting simultaneous pressure inputs, but also recognizing Morse code, braille, and object movement. Furthermore, with dimensionality-reduced feature extraction and learning, our system can recognize and memorize handwritten alphabets and words, providing a promising approach towards e-skin, neurorobotics and human-machine interaction technologies.

The development of neuromorphic architectures depends on the engineering of new functional materials and material interfaces. Here, we present a study on organic ferroelectric tunnel junctions (FTJs) comprising a metal/ferroelectric/semiconductor stack with varying charge carrier density in the semiconducting electrode and demonstrate fast, volatile switching behavior when the bound polarization charges in the tunnel barrier are insufficiently screened. The manipulation of ferroelectric polarization and depolarization dynamics in our FTJs through pulse magnitude, duration, and delay time constitutes a controlled transition from synaptic behavior to integrate-and-fire neuronal activity. This ability to tune the response of a single memristor device via charge carrier optimization opens pathways for the design of smart electronic neurons.

One of the major challenges for in vivo electrochemical measurements of dopamine (DA) is to achieve selectivity in the presence of interferents, such as ascorbic acid (AA) and uric acid (UA). Complicated multimaterial structures and ill-defined pretreatments have been frequently utilized to enhance selectivity. The lack of control over the realized structures has prevented establishing associations between the achieved selectivity and the electrode structure. Owing to their easily tailorable structure, carbon nanofiber (CNF) electrodes have become promising materials for neurobiological applications. Here, a novel yet simple strategy to control the sensitivity and selectivity of CNF electrodes toward DA is reported. It consists of adjusting the lengths of CNF by modulating the growth phase during the fabrication process while keeping the surface chemistries similar. It was observed that the sensitivity of the CNF electrodes toward DA was enhanced with the increase in the fiber lengths. More importantly, the increase in the fiber length induced (i) an anodic shift in the DA oxidation peak and (ii) a cathodic shif t in the AA oxidation peak. As the UA oxidation peak remained unaffected at high anodic potentials, the electrodes with long CNFs showed excellent selectivity. Electrodes without proper fibers showed only a single broad peak in the solution of AA, DA, and UA, completely lacking the ability to discriminate DA. Hence, the simple strategy of controlling CNF length without the need to carry out any complex chemical treatments provides us a feasible and robust route to fabricate electrode materials for neurotransmitter detection with excellent sensitivity and selectivity.

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