Two dimensional electrophysiological characterization of human pluripotent stem cell-derived cardiomyocyte system.

Stem cell-derived cardiomyocytes provide a promising tool for human developmental biology, regenerative therapies, disease modeling, and drug discovery. As human pluripotent stem cell-derived cardiomyocytes remain functionally fetal-type, close monitoring of electrophysiological maturation is critical for their further application to biology and translation. However, to date, electrophysiological analyses of stem cell-derived cardiomyocytes has largely been limited by biologically undefined factors including 3D nature of embryoid body, sera from animals, and the feeder cells isolated from mouse. Large variability in the aforementioned systems leads to uncontrollable and irreproducible results, making conclusive studies difficult. In this report, a chemically-defined differentiation regimen and a monolayer cell culture technique was combined with multielectrode arrays for accurate, real-time, and flexible measurement of electrophysiological parameters in translation-ready human cardiomyocytes. Consistent with their natural counterpart, amplitude and dV/dtmax of field potential progressively increased during the course of maturation. Monolayer culture allowed for the identification of pacemaking cells using the multielectrode array platform and thereby the estimation of conduction velocity, which gradually increased during the differentiation of cardiomyocytes. Thus, the electrophysiological maturation of the human pluripotent stem cell-derived cardiomyocytes in our system recapitulates in vivo development. This system provides a versatile biological tool to analyze human heart development, disease mechanisms, and the efficacy/toxicity of chemicals

Comprehensive Analysis of Escape-Cone Losses from Luminescent Waveguides

Luminescent waveguides (LWs) occur in a wide range of applications, from solar concentrators to doped fiber amplifiers. Here we report a comprehensive analysis of escape-cone losses in LWs, which are losses associated with internal rays making an angle less than the critical angle with a waveguide surface. For applications such as luminescent solar concentrators, escape-cone losses often dominate all others. A statistical treatment of escape-cone losses is given accounting for photoselection, photon polarization, and the Fresnel relations, and the model is used to analyze light absorption and propagation in waveguides with isotropic and orientationally aligned luminophores. The results are then compared to experimental measurements performed on a fluorescent dye-doped poly(methyl methacrylate) waveguide

Spoken Digit Classification by In-Materio Reservoir Computing with Neuromorphic Atomic Switch Networks

Atomic Switch Networks (ASN) comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly-interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing (RC). This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data (FSDD). These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.Comment: 11 pages, 7 figure

Beyond Moore neuromorphic chips: harnessing complexity in atomic switch networks for alternative computing

The invention of the internet began the age of information as well as exponentially increased the number of complex systems in our world. As the age of information comes to an end, so does the persevering trend known as Moore's Law. This means that the number of circuit elements on an integrated chip will no longer double every two years, nor will the processing speed of computers. Personal computers utilize the Von Neumann architecture which separates storage from processing. This separation causes information transfer lags as a computer processes information much faster than it can be fetched from information storage. Thus, to circumvent both the limitations on elemental packing, and areal density a movement into neuromorphic hardware has occurred. Neuromorphic chips seek to emulate brain-like processing of information through low-power, highly parallel, densely interconnected, and closely packed individual elements which have a non-intuitive entangled relationship. This work explored the potential of atomic switch networks (ASNs) for reservoir, natural, and unconventional computing, provides evidence for ASNs as complex adaptive systems operating in and around the edge of chaos, presents a new material for use in ASNs, and evaluates spoken digit recognition using reservoir computing. A second project herein explores the maturation of human pluripotent stem cell-derived cardiomyocytes for use in studying heart disease, which is the leading cause of death in the world. Maturation of these cells is significant to the field. Via a chemically defined differentiation regimen with a monolayer cell culture technique on top of a multi-electrode array for real-time measurements of electrophysiological properties, in vivo development was reproduced. Both systems described above required data science analysis of time-series multi-electrode array information

Beyond Moore neuromorphic chips: harnessing complexity in atomic switch networks for alternative computing

The invention of the internet began the age of information as well as exponentially increased the number of complex systems in our world. As the age of information comes to an end, so does the persevering trend known as Moore's Law. This means that the number of circuit elements on an integrated chip will no longer double every two years, nor will the processing speed of computers. Personal computers utilize the Von Neumann architecture which separates storage from processing. This separation causes information transfer lags as a computer processes information much faster than it can be fetched from information storage. Thus, to circumvent both the limitations on elemental packing, and areal density a movement into neuromorphic hardware has occurred. Neuromorphic chips seek to emulate brain-like processing of information through low-power, highly parallel, densely interconnected, and closely packed individual elements which have a non-intuitive entangled relationship. This work explored the potential of atomic switch networks (ASNs) for reservoir, natural, and unconventional computing, provides evidence for ASNs as complex adaptive systems operating in and around the edge of chaos, presents a new material for use in ASNs, and evaluates spoken digit recognition using reservoir computing. A second project herein explores the maturation of human pluripotent stem cell-derived cardiomyocytes for use in studying heart disease, which is the leading cause of death in the world. Maturation of these cells is significant to the field. Via a chemically defined differentiation regimen with a monolayer cell culture technique on top of a multi-electrode array for real-time measurements of electrophysiological properties, in vivo development was reproduced. Both systems described above required data science analysis of time-series multi-electrode array information

Non-Temporal Logic Performance of an Atomic Switch Network

Efforts to achieve a low-power, dynamically complex system become crucial as CMOS fabrication limits are realized. Atomic Switch Networks (ASNs) provide fabrication advantages over traditional CMOS through the combination of top-down and bottom-up techniques, leading to densely inter-connected networks of atomic switches. ASNs show emergent behaviors through the interaction of individual non-linear elements. These properties make ASNs suitable for alternative computational paradigms, such as neuromorphic or reservoir computing. This work examined ASNs\u27 ability to perform Boolean logic operations using non-temporal inputs based on randomized Boolean input streams. Zero and one bits were converted to negative and positive DC voltage pulses, respectfully. Next, a linear readout layer was applied to an array of voltage outputs from the device to reconstruct target output signals for the given task. ASNs produced nearly perfect results at low voltages for AND, OR, and NAND with more than 95% confidence. XOR, which requires non-linearity to solve, was able to be partially solved at high voltages with more than 95% confidence. As opposed to previous works which have investigated temporal computation in ASNs, this work was the first to demonstrate semi-predictable, non-temporal, non-linear behavior within the device. Results demonstrated that the device connectivity is complete enough to perform complex computations

Sterically Engineered Perylene Dyes for High Efficiency Oriented Fluorophore Luminescent Solar Concentrators

Sterically Engineered Perylene Dyes for High Efficiency Oriented Fluorophore Luminescent Solar Concentrator