Name
Abstract | ||
|---|---|---|
Many activity dependent learning rules have been proposed in order to model long-term potentiation (LTP). Our aim is to derive a spike time dependent learning rule from a probabilistic optimality criterion. Our approach allows us to obtain quantitative results in terms of a learning window. This is done by maximising a given likelihood function with respect to the synaptic weights. The resulting weight adaptation is compared with experimental results. |
Year | Venue | Keywords |
|---|---|---|
2003 | ICANN | probabilistic point,probabilistic optimality criterion,spike time,likelihood function,optimal hebbian learning,activity dependent learning rule,synaptic weight,dependent learning rule,resulting weight adaptation,quantitative result,long-term potentiation,hebbian learning,long term potentiation |
Field | DocType | Volume |
Likelihood function,Optimality criterion,Spike train,Computer science,Algorithm,Hebbian theory,Learning rule,Artificial intelligence,Probabilistic logic,Synaptic weight,Leabra,Machine learning | Conference | 2714 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-40408-2 | 9 |
PageRank | References | Authors |
0.71 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jean-pascal Pfister | 1 | 150 | 13.64 |
| David Barber | 2 | 404 | 45.57 |
| Wulfram Gerstner | 3 | 2437 | 410.08 |