We report the design in CMOS technology and the experimental characterization of an analog spiking neural network with on-chip unsupervised learning. Long-term synaptic memory is implemented using a floating-gate device in a standard 150 nm CMOS process. The neurons are operated with a voltage supply of only 0.4V, allowing an extremely low power dissipation with an energy dissipation per synaptic operation of about 55 fJ. The CMOS chip includes the circuits for implementing real-time learning of the network based on the Spike Time Dependent Plasticity algorithm. During the learning, the neurons produce pulses of ±4.5 V that change the synaptic weight by activating tunneling currents to change the charge in the floating gates.
<div>Among the many manuscripts at the Freer Gallery of Art is a lavishly illuminated copy of the <i>Gulistan</i> of Sa‘di (F1998.5). This manuscript was transcribed in an elegant nasta‘liq script by renowned calligrapher Sultan ‘Ali Mashhadi in Herāt (present-day Afghanistan) in 1468, but much of its history is unknown. The text includes six paintings that were added in the seventeenth century during the reign of Mughal emperor Shah Jahan in India. Stains on the versos of the painted pages provide tantalizing traces of the existence of earlier illustrations underneath the Mughal ones. A technical study incorporating infrared and ultraviolet imaging, X-ray computed radiography, and targeted pigment analyses has revealed new information about these preexisting paintings. The size of the figures and the intermediate changes to the compositions suggest there were several working phases for the manuscript. A revised chronology now includes these phases and further enriches our understanding of this complex manuscript. From a technical standpoint, this research highlights both the challenges and undeniable potential of imaging technology for the study of Islamic manuscript paintings, many of which have been reworked at various times in their history.</div>
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