Multitasking associative networks
Physical Review Letters, ISSN: 0031-9007, Vol: 109, Issue: 26, Page: 268101
2012
- 93Citations
- 54Captures
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.
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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
- Citations93
- Citation Indexes93
- CrossRef93
- 92
- Captures54
- Readers54
- 54
Article Description
We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzmann machine and we show its thermodynamical equivalence to an associative working memory able to retrieve several patterns in parallel without falling into spurious states typical of classical neural networks. We focus on systems processing in parallel a finite (up to logarithmic growth in the volume) amount of patterns, mirroring the low-level storage of standard Amit-Gutfreund-Sompolinsky theory. Results obtained through statistical mechanics, the signal-to-noise technique, and Monte Carlo simulations are overall in perfect agreement and carry interesting biological insights. Indeed, these associative networks pave new perspectives in the understanding of multitasking features expressed by complex systems, e.g., neural and immune networks. © 2012 American Physical Society.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84871816033&origin=inward; http://dx.doi.org/10.1103/physrevlett.109.268101; http://www.ncbi.nlm.nih.gov/pubmed/23368622; https://link.aps.org/doi/10.1103/PhysRevLett.109.268101; http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevLett.109.268101/fulltext; http://link.aps.org/article/10.1103/PhysRevLett.109.268101
American Physical Society (APS)
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