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Curr Opin Neurobiol. 2015 Jun;32:87-94. doi: 10.1016/j.conb.2015.01.006. Epub 2015 Jan 29.

Computational models in the age of large datasets.

Author information

1
Biology Department and Volen Center, Brandeis University, Waltham, MA 02454, United States.
2
Biology Department and Volen Center, Brandeis University, Waltham, MA 02454, United States. Electronic address: marder@brandeis.edu.

Abstract

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.

PMID:
25637959
PMCID:
PMC4517826
DOI:
10.1016/j.conb.2015.01.006
[Indexed for MEDLINE]
Free PMC Article

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