Computational models in the age of large datasets
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 fin...
Saved in:
| Main Authors: | , , |
|---|---|
| Language: | English |
| Published: |
Elsevier
2019
|
| Online Access: | https://demo7.dspace.org/handle/123456789/467 |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1860822453095235584 |
|---|---|
| author | O'Leary, Timothy Sutton, Alexander C Marder, Eve |
| author_browse | Marder, Eve O'Leary, Timothy Sutton, Alexander C |
| author_facet | O'Leary, Timothy Sutton, Alexander C Marder, Eve |
| author_sort | O'Leary, Timothy |
| collection | DSpace |
| 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. |
| id | oai:localhost:123456789-467 |
| institution | DSPACE.FCHPT |
| language | English |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | oai:localhost:123456789-4672021-04-07T16:30:12Z Computational models in the age of large datasets O'Leary, Timothy Sutton, Alexander C Marder, Eve 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. 2019-04-26T08:57:22Z 2019-04-26T08:57:22Z 29/01/15 https://demo7.dspace.org/handle/123456789/467 en Elsevier |
| spellingShingle | O'Leary, Timothy Sutton, Alexander C Marder, Eve Computational models in the age of large datasets |
| title | Computational models in the age of large datasets |
| title_full | Computational models in the age of large datasets |
| title_fullStr | Computational models in the age of large datasets |
| title_full_unstemmed | Computational models in the age of large datasets |
| title_short | Computational models in the age of large datasets |
| title_sort | computational models in the age of large datasets |
| url | https://demo7.dspace.org/handle/123456789/467 |
| work_keys_str_mv | AT olearytimothy computationalmodelsintheageoflargedatasets AT suttonalexanderc computationalmodelsintheageoflargedatasets AT mardereve computationalmodelsintheageoflargedatasets |