Speaker of Workshop 4
Will talk about: Semi-automated approaches for drawing inferences from the vast neurophysiology literature
Shreejoy Tripathy is a post-doc in the Centre for High-Throughput Biology at the University of British Columbia. He received his PhD in neural computation from Carnegie Mellon University in 2013, working with Nathan Urban on the form and function of neuron electrophysiological diversity as an NSF graduate fellow. Shreejoy is passionate about using principles of open science and open data to improve research practices in neuroscience. His research uses a combination of data mining, machine learning, and domain knowledge to link disparate data modalities in neuroscience, with a focus on neuron electrophysiology and genomics. He is also involved in developing common standards and terminologies to faciliate data reuse and sharing.
Over the past decade, neurophysiology saw a data explosion as groups worldwide have published thousands of articles on the biophysical properties of a rich diversity of neuron types. In this talk, I will discuss NeuroElectro.org, an effort to extract this information by employing semi-automated literature text-mining algorithms. I will describe approaches for normalizing and structuring such heterogeneous data. I will also describe meta-analyses where we have combined this data with other data sets, such as Allen Institute gene expression atlases, to explore the scope and genetic origins of brain-wide neuronal electrophysiological diversity. Lastly, I will discuss example use cases of NeuroElectro data, including using the public API to provide parmaters to help build and constrain generic computational neuron models.