In numerical simulations, we confirm that the best task performance is indeed achieved at the criticality. This is an indicator of a certain duality between the task performance and the criticality observed in many previous results5,12,15,16. Our result is, however, the first one in which criticality signatures have been obtained without any activity-dependent plasticity rules, but following the task performance solely. Additionally, in contrast to the existing results, we show the persistence of criticality signatures in the STO-network under structured periodic input, whereas such input breaks criticality and a sufficient noise is necessary for the occurrence of the criticality signatures for a class of self-organized spiking recurrent neural networks16. At the end of the paper, we analyze the trained network against task-independent information-theoretic measures and provide a qualitative characterization of the interconnection graph evolution during training.
Continue reading your article with a FrontPage News Subscription
Already a Subscriber? Sign In
VIEW SUBSCRIPTION OPTIONS
Support independent journalism by subscribing