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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00503688" target="_blank" >RIV/67985807:_____/19:00503688 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-030-30487-4_62" target="_blank" >http://dx.doi.org/10.1007/978-3-030-30487-4_62</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-30487-4_62" target="_blank" >10.1007/978-3-030-30487-4_62</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-05704S" target="_blank" >GA19-05704S: FoNeCo: Analytical Foundations of Neurocomputing</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. Proceedings, Part I

ISBN

978-3-030-30486-7

ISSN

0302-9743

e-ISSN

—

Number of pages

13

Pages from-to

806-818

Publisher name

Springer

Place of publication

Cham

Event location

Munich

Event date

Sep 17, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—