title: Computational models in the age of large datasets.
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.
authors: Biology Department and Volen Center, Brandeis University, Waltham, MA 02454, United States.
authors: Timothy
authors: T
authors: O'Leary
authors: Biology Department and Volen Center, Brandeis University, Waltham, MA 02454, United States.
authors: Alexander C
authors: AC
authors: Sutton
authors: Biology Department and Volen Center, Brandeis University, Waltham, MA 02454, United States. Electronic address: marder@brandeis.edu.
authors: Eve
authors: E
authors: Marder
mesh_terms: D000818:Animals; D066264:Datasets as Topic; D008962:Models, Theoretical; D009482:Neurophysiology / Q000379:methods* / Q000592:standards
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