|Title||State prediction: a constructive method to program recurrent neural networks|
|Publication Type||Conference Paper|
|Year of Publication||2011|
|Authors||Reinhart F, Steil JJ|
|Conference Name||Artificial Neural Networks and Machine Learning – ICANN 2011 : 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I|
We introduce a novel technique to program desired state sequences into recurrent neural networks in one shot. The basic methodology and its scalability to large and input-driven networks is demonstrated by shaping attractor landscapes, transient dynamics and programming limit cycles. The approach unifies programming of transient and attractor dynamics in a generic framework.