In the Big Data era, the volume of data is exploding, putting forward a new challenge to existing computer systems. This project aims at creating high performance and energy efficient accelerators to support real-time neural. Recent years, the number of simultaneously recorded neurons has witnessed exponential increase. ![]() It opens up new research opportunities for neuroscientific experiments in closed-loop for understanding how brain works. ![]() The miniaturized fluorescence microscope (Miniscope) and the tetrodes assembly are emerging techniques in observing the activity of a large population of neuros in vivo. The goal of this research project is to make apply the domain-.Ĭustomized Computing for Brain Research and Brain-Inspired Computingĭirection 1: Real-Time Neural Signal Processing for Closed-Loop Neurofeedback Applications. In the era of big data, many applications present siginificant compuational challenges. For example, in the field of bio-infomatics, the computation demand for personalized cancer treatment is prohibitively high for the general-purpose computing technologies, as tumor heterogeneity requires great sequencing depths, structural aberrations are difficult to detect with today’s algorithms, and the tumor has the ability to evolve, meaning the same tumor might be assayed a great many times during the course of treatment.
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