Abeles M et al. (1995). Cortical activity flips among quasi-stationary states. Proceedings of the National Academy of Sciences of the United States of America. 92 [PubMed]

Bedau MA. (2005). Artificial life: more than just building and studying computational systems. Artificial life. 11 [PubMed]

Bengio Y, Simard P, Frasconi P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks. 5 [PubMed]

Bohte S, Kok J, La_Poutre H. (2002). Error-backpropagation in temporally encoded networks of spiking neurons Neurocomputing. 48

Bohte SM. (2003). Spiking neural networks Unpublished doctoral dissertation.

Casey M. (1996). The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. Neural computation. 8 [PubMed]

Chen D et al. (1992). Learning and extracting finite state automata with second-order recurrent neural networks Neural Comput. 4

Floreano D, Mattiussi C. (2001). Evolution of spiking neural controllers for autonomous vision-based robots Evolutionary robotics IV.

Forcada ML, Carrasco RC. (1995). Learning the initial state of a second-order recurrent neural network during regular-language inference Neural Comput. 7

Frasconi P, Gori M, Maggini M, Soda G. (1996). Representation of finite state automata in recurrent radial basis function networks Mach Learn. 23

Gerstner W. (1995). Time structure of the activity in neural network models. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 51 [PubMed]

Gerstner W. (1999). Spiking neurons Pulsed Neural Networks.

Giles CL, Lawrence S, Fong S. (2000). Natural language grammatical inference with recurrent neural networks IEEE Transactions On Knowledge And Data Engineering. 12

Giles CL, Omlin C. (1996). Extraction of rules from discrete-time recurrent neural networks Neural Networks. 9

Giles CL, Tino P, Horne BG, Collingwood PC. (1998). Finite state machines and recurrent neural networks-automata and dynamical systems approaches Neural networks and pattern recognition.

Hopcroft J, Ullman J. (1979). Introduction to automata theory, languages, and computation.

Jacobsson H. (2005). Rule extraction from recurrent neural networks: A taxonomy and review Neural Comput. 17

Knüsel P, Wyss R, König P, Verschure PF. (2004). Decoding a temporal population code. Neural computation. 16 [PubMed]

Liu Y, Yao X. (1997). Fast evolution strategies Control And Cybernetics. 26

Maass W. (1996). Lower bounds for the computational power of networks of spiking neurons Neural Comput. 8

Maass W, Bishop CM. (1999). Pulsed Neural Networks..

Maass W, Markram H. (2002). Synapses as dynamic memory buffers. Neural networks : the official journal of the International Neural Network Society. 15 [PubMed]

Maass W, Natschlager T. (2002). Spiking neurons and the induction of finite state machines Theoretical Computer Science: Special Issue on Natural Computing. 287

Maass W, Natschläger T, Markram H. (2002). Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural computation. 14 [PubMed]

Martignon L et al. (2000). Neural coding: higher-order temporal patterns in the neurostatistics of cell assemblies. Neural computation. 12 [PubMed]

Mcclelland JL, Servan-Schreiber D, Cleeremans A. (1989). Finite state automata and simple recurrent networks Neural Comput. 1

Moore S. (2002). Back propagation in spiking neural networks Unpublised masters thesis.

Mozer MC. (1994). Neural net architectures for temporal sequence processing Predicting the future and understanding the past.

Natschläger T, Ruf B. (1998). Spatial and temporal pattern analysis via spiking neurons. Network (Bristol, England). 9 [PubMed]

Nádasdy Z, Hirase H, Czurkó A, Csicsvari J, Buzsáki G. (1999). Replay and time compression of recurring spike sequences in the hippocampus. The Journal of neuroscience : the official journal of the Society for Neuroscience. 19 [PubMed]

Omlin CW, Giles CL. (1993). Insertion and refinement of production rules in recurrent neural networks Connection Science. 5

Puskorius GV, Feldkamp LA. (1994). Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE transactions on neural networks. 5 [PubMed]

Rowe J, Hidovic D. (2004). An evolution strategy using a continuous version of the gray-code neighbourhood distribution Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004).

Schrauwen B, Van_Campenhout J. (2004). Extending Spike Prop Proceedings of the International Joint Conference on Neural Networks.

Sontag E, Siegelmann H. (1995). On the computational power of neural nets Journal Of Computer And System Sciences. 50

Tino P, Sajda J. (1995). Learning and extracting initial mealy machines with a modular neural network model Neural Comput. 7

Werbos P. (1989). Generalization of backpropagation with applications to a recurrent gas market model Neural Networks. 1

Yao X. (1999). Evolving artificial neural networks Proceedings Of The IEEE. 87

Zador AM, Deweese MR. (2003). Binary coding in auditory cortex Advances in neural information processing systems. 15