Publication:
Computational models in the age of large datasets

dc.contributor.author OΓÇÖLeary, Timothy
dc.contributor.author Sutton, Alexander C
dc.contributor.author Marder, Eve
dc.date.accessioned 2019-04-26T08:57:22Z
dc.date.available 2019-04-26T08:57:22Z
dc.date.issued 29/01/15
dc.description Technological advances in experimental neuroscience are generating vast quantities of data, from the dynamics of single molecules to the structure and activity patterns of large networks of neurons. How do we make sense of these voluminous, complex, disparate and often incomplete data? How do we find general principles in the morass of detail? Computational models are invaluable and necessary in this task and yield insights that cannot otherwise be obtained. However, building and interpreting good computational models is a substantial challenge, especially so in the era of large datasets. Fitting detailed models to experimental data is difficult and often requires onerous assumptions, while more loosely constrained conceptual models that explore broad hypotheses and principles can yield more useful insights.
dc.identifier.uri https://demo7.dspace.org/handle/10673/467
dc.language en
dc.publisher Elsevier
dc.title Computational models in the age of large datasets
dspace.entity.type Publication
relation.isProjectOfPublication 7716c5a4-4d25-4535-8477-eececcb08a23
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