Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks

Autoři: Jérémie Cabessa
doc. RNDr. Jiří Šíma, DSc.
Rok vydání: 2019
Typ publikace: Konferenční příspěvek (mezinárodní konf.)
Periodikum: Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I
Projekty: FoNeCo: Analytické základy neurovýpočtů
Bibliografická citace: CABESSA, Jérémie, ŠÍMA, Jiří. Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks. In: TETKO, I. V., KŮRKOVÁ, V., KARPOV, P., THEIS, F., eds. Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I. Cham: Springer, 2019, S. 806-818. Lecture Notes in Computer Science, 11727. ISBN 978-3-030-30486-7. ISSN 0302-9743
Dostupné z: doi:10.1007/978-3-030-30487-4_62
DOI: 10.1007/978-3-030-30487-4_62
ID: 503688

Abstrakt

Synfire rings are important neural circuits capable of conveying synchronous, temporally precise and self-sustained activities in a robust manner. We describe an optimal-size implementation of finite state automata with neural networks composed of synfire rings. More precisely, given any finite automaton, we build a corresponding neural network partly composed of synfire rings capable of simulating it. The synfire ring activities encode the successive states of the automaton throughout its computation. The robustness of the network results from its architecture, which is composed of synfire rings and duplicated core components. In addition, the network's size is asymptotically optimal: for an automaton with n states, the network has theta (√n) cells.