Title |
Challenges of understanding brain function by selective modulation of neuronal subpopulations
|
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Published in |
Trends in Neurosciences, July 2013
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DOI | 10.1016/j.tins.2013.06.005 |
Pubmed ID | |
Authors |
Arvind Kumar, Ioannis Vlachos, Ad Aertsen, Clemens Boucsein |
Abstract |
Neuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity. We demonstrate limits of the selective modulation approach and argue that, even though highly successful in some instances, the approach fails in networks with complex connectivity. We argue to refine experimental techniques by carefully considering the structural features of the neuronal networks involved. Such methods could dramatically increase the effectiveness of selective modulation and may lead to a mechanistic understanding of principles underlying brain function. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 5% |
France | 4 | 2% |
Germany | 4 | 2% |
Japan | 3 | 2% |
Switzerland | 2 | 1% |
China | 2 | 1% |
Brazil | 1 | <1% |
United Kingdom | 1 | <1% |
Russia | 1 | <1% |
Other | 3 | 2% |
Unknown | 167 | 84% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 54 | 27% |
Researcher | 48 | 24% |
Student > Master | 22 | 11% |
Student > Doctoral Student | 16 | 8% |
Professor | 15 | 8% |
Other | 28 | 14% |
Unknown | 15 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 67 | 34% |
Neuroscience | 47 | 24% |
Medicine and Dentistry | 14 | 7% |
Psychology | 13 | 7% |
Engineering | 12 | 6% |
Other | 26 | 13% |
Unknown | 19 | 10% |