Spatio-temporal dynamics of visual information representation and transformation in brain networks resolving algorithmic functions
bioRxiv, ISSN: 2692-8205
2021
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting 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
- Mentions1
- News Mentions1
- 1
Most Recent News
Different computations over the same inputs produce selective behavior in algorithmic brain networks
Abstract A key challenge in neuroimaging remains to understand where, when, and now particularly how human brain networks compute over sensory inputs to achieve behavior.
Article Description
A key challenge in systems neuroscience remains to understand where, when and how mass brain signals that reflect network activity dynamically represent, transmit and transform sensory information for task behavior. Here, we used the classic XOR, OR and AND that imply a different computation on the same inputs for correct task behavior. We disentangled MEG source activity into three distinct information processes that linearly represents each input before nonlinearly integrating them for task behavior.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know