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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Molter, C. Salihoglu, U. Bersini, H. |
| Copyright Year | 2007 |
| Description | Author affiliation: Brain Sci. Inst., Lab. for Dynamics of Emergent Intell., RIKEN, Wako (Molter, C.) |
| Abstract | How information and more particularly memories are represented in brain dynamics is still an open question. By using, a recurrent network receiving a stimulus dependent external input, the author have demonstrated that the use of limit cycle attractors encompass in many aspects the limitations of fixed points attractors and gives better correspondence with neurophysiological facts. A main outcome of this perspective is the apparition of chaotic trajectories: instead of the overwhelming presence of spurious attractors, chaotic dynamics shows up when facing ambiguous situation. Contrary to intuition, many studies reported that noise can have beneficial effects in dynamical systems. Inline with these studies, it is demonstrated here how stochastic noise can make converge the chaotic trajectories to the expected limit cycle attractors and accordingly can improve consequently the retrieval performance. This noise induced retrieval enhancement is very dependent of the type of chaotic dynamics which is function of how information is coded. |
| Starting Page | 1458 |
| Ending Page | 1463 |
| File Size | 3250974 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424413799 |
| ISSN | 10987576 |
| DOI | 10.1109/IJCNN.2007.4371173 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-08-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Stochastic resonance Chaos Limit-cycles Information retrieval Encoding Noise level Signal to noise ratio Biological neural networks Artificial neural networks Neurons |
| Content Type | Text |
| Resource Type | Article |
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