Ultralow power artificial synapses using nanotextured magnetic Josephson junctions

Richardson diffusion in neurons

The dynamics of an initial wave packet affected by random noise is considered in the framework of a comb model. The model is relevant to a diffusion problem in neurons where the transport of ions can be accelerated by an external random field due to synapse fluctuations. In the present specific case, it acts as boundary conditions, which lead to a reaction transport equation with multiplicative noise. The temporal behavior of the mean squared displacement is estimated analytically, and it is shown that the spreading of the initial wave packet corresponds to Richardson diffusion.

Characterization of Uniformity in Nb/NbxSi1-x/Nb Josephson Junctions

The uniformity of the barriers in Josephson junctions (JJs) is a critical parameter in determining performance and operating margins for a wide variety of superconducting electronic circuits. We present an automated measurement system capable of measuring individual JJs across a 1 × 1 cm die at both ambient temperature and 4 K. This technique allows visualization of the spatial variation over a large area of the critical electrical properties of the junctions and allows for the direct correlation between room-temperature (RT) resistance and low temperature properties. The critical current variation of NbxSi1-x (x = 15%) barriers is found to be about 2.6% (one standard deviation) for 1024 junctions across an individual die and only weakly correlates with RT resistance measurements.

Analog control with two Artificial Axons

The artificial axon is a recently introduced synthetic assembly of supported lipid bilayers and voltage gated ion channels, displaying the basic electrophysiology of nerve cells. Here we demonstrate the use of two artificial axons as control elements to achieve a simple task. Namely, we steer a remote control car towards a light source, using the sensory input dependent firing rate of the axons as the control signal for turning left or right. We present the result in the form of the analysis of a movie of the car approaching the light source. In general terms, with this work we pursue a constructivist approach to exploring the nexus between machine language at the nerve cell level and behavior.

Illumination Time Dependent Learning in Dye Sensitized Solar Cells

Learning through vision is an essential skill for intelligent machines. In an attempt to implement this highly complex feature at low energy cost, a dye-sensitized solar cell is proposed that learns using illumination time as a cue. Particularly, the device alters its photocurrent and memorizes this change in dependence of light exposure duration. This behavior parallels synaptic learning that also requires continuous or repeated electrical stimuli as triggers. Therefore, such optically learning solar cells may serve as promising building blocks in optoelectronic neural networks, potentially enabling visually learning electronics operating at negligible energy consumption and minimal hardware complexity.

Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing

Inspired by the biological neuromorphic system, which exhibits a high degree of connectivity to process huge amounts of information, photonic memory is expected to pave a way to overcome the von Neumann bottleneck for nonconventional computing. Here, a photonic flash memory based on all-inorganic CsPbBr₃ perovskite quantum dots (QDs) is demonstrated. The heterostructure formed between the CsPbBr₃ QDs and semiconductor layer serves as a basis for optically programmable and electrically erasable characteristics of the memory device. Furthermore, synapse functions including short-term plasticity, long-term plasticity, and spike-rate-dependent plasticity are emulated at the device level. The photonic potentiation and electrical habituation are implemented and the synaptic weight exhibits multiple wavelength response from 365, 450, 520 to 660 nm. These results may locate the stage for further thrilling novel advances in perovskite-based memories.