The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. Export

@article{citeulike:741333, abstract = {Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.}, address = {Department of Mathematics, University of California, San Diego, La Jolla 92093, USA.}, author = {Casey, M.}, citeulike-article-id = {741333}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/8768390}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=8768390}, day = {15}, issn = {0899-7667}, journal = {Neural Comput}, keywords = {ctrnns}, month = {August}, number = {6}, pages = {1135--1178}, posted-at = {2006-07-14 03:42:04}, priority = {4}, title = {The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/8768390}, volume = {8}, year = {1996} }

TY - JOUR ID - citeulike:741333 L3 - citeulike-article-id:741333 N2 - Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure. IS - 6 JF - Neural Comput SN - 0899-7667 AD - Department of Mathematics, University of California, San Diego, La Jolla 92093, USA. EP - 1178 TI - The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. VL - 8 SP - 1135 KW - ctrnns AU - Casey, M PY - 1996/08/15/ UR - http://view.ncbi.nlm.nih.gov/pubmed/8768390 ER -