The DOI matches its Crossref record; 167 of the 167 references it cites were found in a registry or an index.
Large-scale neural recordings call for new insights to link brain and behavior
Open accessAbstract
Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. In the present review, we first describe emerging tools and technologies being used to probe large-scale brain activity and new approaches to characterize behavior in the context of such measurements. We next highlight insights obtained from large-scale neural recordings in diverse model systems, and argue that some of these pose a challenge to traditional theoretical frameworks. Finally, we elaborate on existing modeling frameworks to interpret these data, and argue that the interpretation of brain-wide neural recordings calls for new theoretical approaches that may depend on the desired level of understanding. These advances in both neural recordings and theory development will pave the way for critical advances in our understanding of the brain. Neuroscientists can measure activity from more neurons than ever before, garnering new insights and posing challenges to traditional theoretical frameworks. New frameworks may help researchers use these observations to shed light on brain function.
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An integrative data-driven model simulating C. elegans brain, body and environment interactions
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“Therefore, no data-driven model currently meets all requirements of an integrative data-driven model, although such integration is urgently needed to progress our understanding of neural control mechanisms 11 .”
Metadata
- Authors
- Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K.
- Venue
- Nature Neuroscience
- Published
- 2022-01
- Volume
- 25
- Issue
- 1
- Pages
- 11-19
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W3141799644
- Cited by
- 328
- References
- 167
- FWCI
- 31.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
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