2003/07/28 by Frank Emmert-Streib, Emmert-Streib, Frank
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Biological sciences #FOS: Physical sciences #Quantitative Biology (q-bio) #cond-mat.dis-nn #q-bio
paper · pdf · doi:10.48550/arxiv.cond-mat/0307667
7 pages, 4 figures
arxiv created 2003/07/28 · arxiv updated 2009/12/01
We demonstrate that our recently introduced stochastic Hebb-like learning rule is capable of learning the problem of timing in general network topologies generated by an algorithm of Watts and Strogatz. We compare our results with a learning rule proposed by Bak and Chialvo and obtain not only a significantly better convergence behavior but also a dependence of the presentation order of the patterns to be learned by introduction of an additional degree of freedom which allows the neural network to select the next pattern itself whereas the learning rule of Bak and Chialvo stays uneffected. This dependence offers a bidirectional communication between a neuronal and a behavioural level and hence completes the action-perception-cycle which is a characteristics of any living being with a brain.