Name
Abstract | ||
|---|---|---|
We consider the multitasking associative network in the low-storage limit and we study its phase diagram with respect to the noise level T and the degree d of dilution in pattern entries. We find that the system is characterized by a rich variety of stable states, including pure states, parallel retrieval states, hierarchically organized states and symmetric mixtures (remarkably, both even and odd), whose complexity increases as the number of patterns P grows. The analysis is performed both analytically and numerically: Exploiting techniques based on partial differential equations, we are able to get the self-consistencies for the order parameters. Such self-consistency equations are then solved and the solutions are further checked through stability theory to catalog their organizations into the phase diagram, which is outlined at the end. This is a further step towards the understanding of spontaneous parallel processing in associative networks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neunet.2013.09.008 | Neural Networks |
Keywords | Field | DocType |
low-storage limit,phase diagram,complexity increase,associative network,multitasking networks,hierarchically organized state,attractor network,hopfield model,statistical mechanics,noise level,exploiting technique,multitasking associative network,neuronal threshold noise,spontaneous parallel processing,parallel retrieval state | Attractor,Statistical mechanics,Associative property,Computer science,Noise level,Phase diagram,Artificial intelligence,Human multitasking,Partial differential equation,Machine learning,Stability theory | Journal |
Volume | Issue | ISSN |
49 | 1 | 1879-2782 |
Citations | PageRank | References |
3 | 0.46 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elena Agliari | 1 | 18 | 4.19 |
| Adriano Barra | 2 | 43 | 8.13 |
| Andrea Galluzzi | 3 | 16 | 2.11 |
| Marco Isopi | 4 | 7 | 1.30 |