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| Hauptseite > Publikationsdatenbank > Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity > print |
| Hauptseite > Publikationsdatenbank > Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity > print |
| 001 | 19275 | ||
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| 024 | 7 | _ | |2 DOI |a 10.3389/fncom.2010.00141 |
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| 084 | _ | _ | |2 WoS |a Neurosciences |
| 100 | 1 | _ | |0 P:(DE-Juel1)VDB103864 |a Potjans, W |b 0 |u FZJ |
| 245 | _ | _ | |a Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| 260 | _ | _ | |a Lausanne |b Frontiers Research Foundation |c 2010 |
| 300 | _ | _ | |a 141 |
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| 440 | _ | 0 | |0 23745 |a Frontiers in Computational Neuroscience |v 4 |x 1662-5188 |y 4 |
| 500 | _ | _ | |a We are most grateful to Hans Ekkehard Plesser for language legality consultation. We also thank the editor and the reviewers for the constructive interaction which helped us to considerably improve the integration of our work into the special issue. Partially funded by DIP F1.2, BMBF Grant 01GQ0420 to the Bernstein Center for Computational Neuroscience Freiburg, EU Grant 15879 (FACETS), the Junior Professor Program of Baden-Wurttemberg, "The Next-Generation Integrated Simulation of Living Matter" project, part of the Development and Use of the Next-Generation Supercomputer Project of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan and the Helmholtz Alliance on Systems Biology. Access to supercomputing facility through JUGENE-Grant JINB33. |
| 520 | _ | _ | |a A major puzzle in the field of computational neuroscience is how to relate system-level learning in higher organisms to synaptic plasticity. Recently, plasticity rules depending not only on pre- and post-synaptic activity but also on a third, non-local neuromodulatory signal have emerged as key candidates to bridge the gap between the macroscopic and the microscopic level of learning. Crucial insights into this topic are expected to be gained from simulations of neural systems, as these allow the simultaneous study of the multiple spatial and temporal scales that are involved in the problem. In particular, synaptic plasticity can be studied during the whole learning process, i.e., on a time scale of minutes to hours and across multiple brain areas. Implementing neuromodulated plasticity in large-scale network simulations where the neuromodulatory signal is dynamically generated by the network itself is challenging, because the network structure is commonly defined purely by the connectivity graph without explicit reference to the embedding of the nodes in physical space. Furthermore, the simulation of networks with realistic connectivity entails the use of distributed computing. A neuromodulated synapse must therefore be informed in an efficient way about the neuromodulatory signal, which is typically generated by a population of neurons located on different machines than either the pre- or post-synaptic neuron. Here, we develop a general framework to solve the problem of implementing neuromodulated plasticity in a time-driven distributed simulation, without reference to a particular implementation language, neuromodulator, or neuromodulated plasticity mechanism. We implement our framework in the simulator NEST and demonstrate excellent scaling up to 1024 processors for simulations of a recurrent network incorporating neuromodulated spike-timing dependent plasticity. |
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| 773 | _ | _ | |0 PERI:(DE-600)2452964-3 |a 10.3389/fncom.2010.00141 |g Vol. 4, p. 141 |p 1-17 |q 4<141 |t Frontiers in computational neuroscience |v 4 |x 1662-5188 |y 2010 |
| 856 | 7 | _ | |2 Pubmed Central |u http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2996144 |
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