Citation Relationships



Tino P, Mills AJ (2006) Learning beyond finite memory in recurrent networks of spiking neurons. Neural Comput 18:591-613 [PubMed]

References and models cited by this paper

References and models that cite this paper

Abeles M, Bergman H, Gat I, Meilijson I, Seidemann E, Tishby N, Vaadia E (1995) Cortical activity flips among quasi-stationary states. Proc Natl Acad Sci U S A 92:8616-20 [PubMed]

Bedau MA (2005) Artificial life: more than just building and studying computational systems. Artif Life 11:1-3 [Journal] [PubMed]

Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5:157-66 [Journal] [PubMed]

Bohte S, Kok J, La_Poutre H (2002) Error-backpropagation in temporally encoded networks of spiking neurons Neurocomputing 48:17-37

Bohte SM (2003) Spiking neural networks Unpublished doctoral dissertation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 Comput 8:1135-78 [PubMed]

Cleeremans A, Servan-Schreiber D, Mcclelland JL (1989) Finite state automata and simple recurrent networks Neural Comput 1:372-381

Deweese MR, Zador AM (2003) Binary coding in auditory cortex Advances in neural information processing systems, Becker S:Thrun S:Obermayer K, ed. pp.101

Floreano D, Mattiussi C (2001) Evolution of spiking neural controllers for autonomous vision-based robots Evolutionary robotics IV, Gomi T, ed. pp.38

Forcada ML, Carrasco RC (1995) Learning the initial state of a second-order recurrent neural network during regular-language inference Neural Comput 7:923-930

Frasconi P, Gori M, Maggini M, Soda G (1996) Representation of finite state automata in recurrent radial basis function networks Mach Learn 23:5-32

Gerstner W (1995) Time structure of the activity in neural network models. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 51:738-758 [PubMed]

Gerstner W (1999) Spiking neurons Pulsed Neural Networks, Mass W:Bishop CM, ed. pp.3

Giles CL, Miller CB, Chen D, Chen HH, Sun GZ, Lee YC (1992) Learning and extracting finite state automata with second-order recurrent neural networks Neural Comput 4:393-405

Giles CL, Omlin CW (1993) Insertion and refinement of production rules in recurrent neural networks Connection Science 5:307-377

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 17:1223-1263

Knüsel P, Wyss R, König P, Verschure PF (2004) Decoding a temporal population code. Neural Comput 16:2079-100 [Journal] [PubMed]

Lawrence S, Giles CL, Fong S (2000) Natural language grammatical inference with recurrent neural networks IEEE Transactions On Knowledge And Data Engineering 12:126-140

Maass W (1996) Lower bounds for the computational power of networks of spiking neurons Neural Comput 8:1-40

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

Maass W, Markram H (2002) Synapses as dynamic memory buffers. Neural Netw 15:155-61 [PubMed]

Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14:2531-60 [Journal] [PubMed]

Martignon L, Deco G, Laskey K, Diamond M, Freiwald W, Vaadia E (2000) Neural coding: higher-order temporal patterns in the neurostatistics of cell assemblies. Neural Comput 12:2621-53 [PubMed]

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, Weigend A:Gershenfeld N, ed. pp.243

Nádasdy Z, Hirase H, Czurkó A, Csicsvari J, Buzsáki G (1999) Replay and time compression of recurring spike sequences in the hippocampus. J Neurosci 19:9497-507 [PubMed]

Natschlager T, Maass W (2002) Spiking neurons and the induction of finite state machines Theoretical Computer Science: Special Issue on Natural Computing 287:251-265

Natschläger T, Ruf B (1998) Spatial and temporal pattern analysis via spiking neurons. Network 9:319-32 [PubMed]

Omlin C, Giles CL (1996) Extraction of rules from discrete-time recurrent neural networks Neural Networks 9:41-51

Puskorius GV, Feldkamp LA (1994) Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE Trans Neural Netw 5:279-97 [Journal] [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) :725-736

Schrauwen B, Van_Campenhout J (2004) Extending Spike Prop Proceedings of the International Joint Conference on Neural Networks :471-476

Siegelmann H, Sontag E (1995) On the computational power of neural nets Journal Of Computer And System Sciences 50:132-150

Tino P, Horne BG, Giles CL, Collingwood PC (1998) Finite state machines and recurrent neural networks-automata and dynamical systems approaches Neural networks and pattern recognition, Dayhoff JE:Omidvar O, ed. pp.171

Tino P, Sajda J (1995) Learning and extracting initial mealy machines with a modular neural network model Neural Comput 7:822-844

Werbos P (1989) Generalization of backpropagation with applications to a recurrent gas market model Neural Networks 1:339-356

Yao X (1999) Evolving artificial neural networks Proceedings Of The IEEE 87:1423-1447

Yao X, Liu Y (1997) Fast evolution strategies Control And Cybernetics 26:467-490

(39 refs)