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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Davies, S. Rast, A. Galluppi, F. Furber, S. |
| Copyright Year | 2011 |
| Description | Author affiliation: School of Computer Science, The University of Manchester, U.K (Davies, S.; Rast, A.; Galluppi, F.; Furber, S.) |
| Abstract | Spike Timing Dependent Plasticity (STDP) is a well known paradigm for learning in neural networks. In this paper we propose a new approach to this problem based on the standard STDP algorithm, with modifications and approximations, that relate the membrane potential with the LTP (Long Term Potentiation) part of the basic STDP rule. On the other side we use the standard STDP rule for the LTD (Long Term Depression) part of the algorithm. We show that on the basis of the membrane potential [5] it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike.We present results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. Through the approximations we suggest in this paper we introduce a learning rule that is easy to implement in simulators and reduces the execution time if compared with the standard STDP rule. |
| Starting Page | 1810 |
| Ending Page | 1817 |
| File Size | 1849064 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033444 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neurons Approximation algorithms Biological neural networks Computational modeling Biological system modeling Biomembranes |
| Content Type | Text |
| Resource Type | Article |
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