Discovering the Computational Relevance of Brain Network Organization
Trends in Cognitive Sciences, ISSN: 1364-6613, Vol: 24, Issue: 1, Page: 25-38
2020
- 52Citations
- 160Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations52
- Citation Indexes52
- 52
- CrossRef33
- Captures160
- Readers160
- 160
Review Description
Understanding neurocognitive computations will require not just localizing cognitive information distributed throughout the brain but also determining how that information got there. We review recent advances in linking empirical and simulated brain network organization with cognitive information processing. Building on these advances, we offer a new framework for understanding the role of connectivity in cognition: network coding (encoding/decoding) models. These models utilize connectivity to specify the transfer of information via neural activity flow processes, successfully predicting the formation of cognitive representations in empirical neural data. The success of these models supports the possibility that localized neural functions mechanistically emerge (are computed) from distributed activity flow processes that are specified primarily by connectivity patterns.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1364661319302402; http://dx.doi.org/10.1016/j.tics.2019.10.005; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85075459391&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/31727507; https://linkinghub.elsevier.com/retrieve/pii/S1364661319302402; https://dx.doi.org/10.1016/j.tics.2019.10.005
Elsevier BV
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