RESUMO
A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector-matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated memristor devices. Based on these results, we conclude that memristor-based computing is the preeminent technology for a radiation detection platform.
RESUMO
We report a new hybrid integration scheme that offers for the first time a nanowire-on-lead approach, which enables independent electrical addressability, is scalable, and has superior spatial resolution in vertical nanowire arrays. The fabrication of these nanowire arrays is demonstrated to be scalable down to submicrometer site-to-site spacing and can be combined with standard integrated circuit fabrication technologies. We utilize these arrays to perform electrophysiological recordings from mouse and rat primary neurons and human induced pluripotent stem cell (hiPSC)-derived neurons, which revealed high signal-to-noise ratios and sensitivity to subthreshold postsynaptic potentials (PSPs). We measured electrical activity from rodent neurons from 8 days in vitro (DIV) to 14 DIV and from hiPSC-derived neurons at 6 weeks in vitro post culture with signal amplitudes up to 99 mV. Overall, our platform paves the way for longitudinal electrophysiological experiments on synaptic activity in human iPSC based disease models of neuronal networks, critical for understanding the mechanisms of neurological diseases and for developing drugs to treat them.