|Title||New Data Science tools for analyzing neural data and computational models|
|Publication Type||Conference Abstract|
|Year of Publication||2016|
|Authors||Meyers, E, Dean, M, Hale, GJ|
|Conference Name||Society for Neuroscience|
As the amount of data collected by neuroscientists continues to increase (Stevenson et al, 2011), new tools are needed to turn this data into insights into about the algorithms that underlie complex behavior (Brown et al, 2004). Here we present our latest research on computational tools we have developed at Hampshire College and at the Center for Brains, Minds and Machines at MIT. In particular, we describe new tools for neural population decoding including a graphical user interface to the Neural Decoding Toolbox (Meyers 2013), methods for analyzing single neurons, and ongoing work on a parallelized population decoding framework that uses R and Apache Spark™ to greatly increase the speed of population decoding. We also discuss CBaaS, which is a distributed platform that allows one to evaluate the effectiveness of different computational models (such as different versions of deep neural networks). These tools will allow researchers to gain deeper insights from the data they collect, and to better assess whether computational models are acting in similar ways to biological systems.
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