DocumentCode :
1945433
Title :
How Stochastic Noise Helps Memory Retrieval in a Chaotic Brain.
Author :
Molter, Colin ; Salihoglu, Utku ; Bersini, Hugues
Author_Institution :
Brain Sci. Inst., Lab. for Dynamics of Emergent Intell., RIKEN, Wako
fYear :
2007
fDate :
12-17 Aug. 2007
Firstpage :
1458
Lastpage :
1463
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.
Keywords :
brain; chaos; limit cycles; neurophysiology; recurrent neural nets; stochastic processes; time-varying systems; brain dynamics; chaotic brain; chaotic dynamics; dynamical systems; limit cycle attractors; memory retrieval; neurophysiological facts; recurrent network; stochastic noise; Artificial neural networks; Biological neural networks; Chaos; Encoding; Information retrieval; Limit-cycles; Neurons; Noise level; Signal to noise ratio; Stochastic resonance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
ISSN :
1098-7576
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
Type :
conf
DOI :
10.1109/IJCNN.2007.4371173
Filename :
4371173
Link To Document :
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