Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity
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 cand...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2010-11-01
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| Series: | Frontiers in Computational Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00141/full |
| _version_ | 1818608048372973568 |
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| author | Wiebke ePotjans Wiebke ePotjans Wiebke ePotjans Abigail Morrison Abigail Morrison Abigail Morrison Markus Diesmann Markus Diesmann Markus Diesmann |
| author_facet | Wiebke ePotjans Wiebke ePotjans Wiebke ePotjans Abigail Morrison Abigail Morrison Abigail Morrison Markus Diesmann Markus Diesmann Markus Diesmann |
| author_sort | Wiebke ePotjans |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-16T14:36:27Z |
| format | Article |
| id | doaj.art-a5ae3889963a427db37e236a2a25ca22 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-16T14:36:27Z |
| publishDate | 2010-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-a5ae3889963a427db37e236a2a25ca222022-12-21T22:28:04ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882010-11-01410.3389/fncom.2010.001411382Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticityWiebke ePotjans0Wiebke ePotjans1Wiebke ePotjans2Abigail Morrison3Abigail Morrison4Abigail Morrison5Markus Diesmann6Markus Diesmann7Markus Diesmann8Research Center JülichAlbert-Ludwigs-University FreiburgRIKEN Brain Science InstituteAlbert-Ludwigs-University FreiburgRIKEN Brain Science InstituteAlbert-Ludwigs-University FreiburgRIKEN Brain Science InstituteRIKEN Computational Science Research ProgramAlbert-Ludwigs-University FreiburgA 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.http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00141/fullsynaptic plasticitymodelingcomputational neuroscienceNeuromodulatorDistributed Computingintegrate-and-fire neurons |
| spellingShingle | Wiebke ePotjans Wiebke ePotjans Wiebke ePotjans Abigail Morrison Abigail Morrison Abigail Morrison Markus Diesmann Markus Diesmann Markus Diesmann Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity Frontiers in Computational Neuroscience synaptic plasticity modeling computational neuroscience Neuromodulator Distributed Computing integrate-and-fire neurons |
| title | Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| title_full | Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| title_fullStr | Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| title_full_unstemmed | Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| title_short | Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| title_sort | enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity |
| topic | synaptic plasticity modeling computational neuroscience Neuromodulator Distributed Computing integrate-and-fire neurons |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2010.00141/full |
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