Predicting Neural Progenitor Cell Networks

نویسنده

  • Nicolas Grandel
چکیده

Predicting neuronal networks from calcium fluorescence data is extremely difficult due to the nature of the data collection. Current methods to overcome this difficulty, while powerful, are somewhat arbitrary, presenting new difficulties in later analysis. Specifically, current ad-hoc methods often make arbitrary distinctions between connected and unconnected neurons. In this project, I attempted to use supervised and unsupervised learning techniques to work around this problem, with varying degrees of success. Of particular interest was the success of the K-Means algorithm, which replicated the success of current methods while avoiding some of their pitfalls.

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تاریخ انتشار 2017