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Will big data yield new mathematics? An evolving synergy with neuroscience.
1Department of Applied Mathematics and Sciences, Khalifa University of Science, Technology, and Research, Abu Dhabi, United Arab Emirates.
IMA Journal of Applied Mathematics
|August 13, 2016
Summary
Massive neuroscience data and advanced analytical methods are driving new mathematical models. This synergy promises to bridge scales from single neurons to whole brain networks, advancing our understanding of neural substrates.
Area of Science:
- Neuroscience
- Mathematics
- Computational Biology
Background:
- Historically, new mathematics has been inspired by natural world insights.
- The field of neuroscience is generating unprecedented volumes of complex data.
Purpose of the Study:
- To explore the interactions between large-scale neuroscience data, analytical methods, and mathematical modeling.
- To investigate how big data can validate or refute neural models across different scales.
- To foster a closer integration between neuroscience and mathematics.
Main Methods:
- Reviewing historical parallels in turbulence modeling.
- Surveying new experimental technologies like optogenetics and functional imaging.
- Analyzing the multi-scale nature of neural data and existing mathematical models.
Main Results:
- Big data can enable model validation/rejection at cellular to brain area levels.
- New analytical techniques for neuroscience data are yielding novel mathematics.
- The multi-scale nature of data may connect single-neuron models (e.g., Hodgkin-Huxley) to network models.
Conclusions:
- A closer liaison between neuroscience and mathematics is envisioned.
- Big data provides a crucial link for developing and testing mathematical models of neural processes.
- This interdisciplinary approach is expected to illuminate uncharted neural substrates.
