%0 Journal Article %1 10.1371/journal.pcbi.1002848 %A Zheng, Pengsheng %A Dimitrakakis, Christos %A Triesch, Jochen %D 2013 %I Public Library of Science %J PLoS Comput Biol %K dynamics myown network organization %N 1 %P e1002848 %R 10.1371/journal.pcbi.1002848 %T Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex %U http://dx.doi.org/10.1371%2Fjournal.pcbi.1002848 %V 9 %X <title>Author Summary</title><p>The computations that brain circuits can perform depend on their wiring. While a wiring diagram is still out of reach for major brain structures such as the neocortex and hippocampus, data on the overall distribution of synaptic connection strengths and the temporal fluctuations of individual synapses have recently become available. Specifically, there exists a small population of very strong and stable synaptic connections, which may form the physiological substrate of life-long memories. This population coexists with a big and ever changing population of much smaller and strongly fluctuating synaptic connections. So far it has remained unclear how these properties of networks in neocortex and hippocampus arise. Here we present a computational model that explains these fundamental properties of neural circuits as a consequence of network self-organization resulting from the combined action of different forms of neuronal plasticity. This self-organization is driven by a rich-get-richer effect induced by an associative synaptic learning mechanism which is kept in check by several homeostatic plasticity mechanisms stabilizing the network. The model highlights the role of self-organization in the formation of brain circuits and parsimoniously explains a range of recent findings about their fundamental properties.</p>

@article{10.1371/journal.pcbi.1002848, abstract = {<title>Author Summary</title><p>The computations that brain circuits can perform depend on their wiring. While a wiring diagram is still out of reach for major brain structures such as the neocortex and hippocampus, data on the overall distribution of synaptic connection strengths and the temporal fluctuations of individual synapses have recently become available. Specifically, there exists a small population of very strong and stable synaptic connections, which may form the physiological substrate of life-long memories. This population coexists with a big and ever changing population of much smaller and strongly fluctuating synaptic connections. So far it has remained unclear how these properties of networks in neocortex and hippocampus arise. Here we present a computational model that explains these fundamental properties of neural circuits as a consequence of network self-organization resulting from the combined action of different forms of neuronal plasticity. This self-organization is driven by a rich-get-richer effect induced by an associative synaptic learning mechanism which is kept in check by several homeostatic plasticity mechanisms stabilizing the network. The model highlights the role of self-organization in the formation of brain circuits and parsimoniously explains a range of recent findings about their fundamental properties.</p>}, added-at = {2013-09-07T11:15:14.000+0200}, author = {Zheng, Pengsheng and Dimitrakakis, Christos and Triesch, Jochen}, biburl = {https://www.bibsonomy.org/bibtex/201edcbfd22ab86b718471e9f09ee4a05/olethros}, doi = {10.1371/journal.pcbi.1002848}, interhash = {09a32f46756719644f6e58e14c743df8}, intrahash = {01edcbfd22ab86b718471e9f09ee4a05}, journal = {PLoS Comput Biol}, keywords = {dynamics myown network organization}, month = {01}, number = 1, pages = {e1002848}, publisher = {Public Library of Science}, timestamp = {2013-09-07T11:15:14.000+0200}, title = {Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1002848}, volume = 9, year = 2013 }