Abstract
In real neuronal networks it is known that neurons are either excitatory or inhibitory. However, it is not known whether all synapses within the subpopulations are plastic. It is interesting to investigate the implications these constraints may have on functionality. Here we investigate highly simplified models of associative memory with a variety of allowed synaptic plasticity regimes. We show that the allowed synaptic plasticity does indeed have a large effect on the performance of the network and that some regimes are much better than others.
Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Publisher | Springer Nature Link |
Pages | 136-142 |
Number of pages | 7 |
Volume | 7223 LNCS |
ISBN (Electronic) | 978-3-642-28792-3 |
ISBN (Print) | 978-3-642-28791-6 |
DOIs | |
Publication status | Published - 2012 |
Event | IPCAT 2012 - Cambridge, United Kingdom Duration: 31 Mar 2012 → … |
Conference
Conference | IPCAT 2012 |
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Country/Territory | United Kingdom |
City | Cambridge |
Period | 31/03/12 → … |