ArticleBiological cybernetics2021
Excitable networks for finite state computation with continuous time recurrent neural networks.
Article in Biological cybernetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.
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1 citing paper in PubMed.
- English Lexical Analysis System of Machine Translation Based on Simple Recurrent Neural Network.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Continuous time recurrent neural networks (CTRNN) are systems of coupled ordinary differential equations that are simple enough to be insightful for describing learning and computation, from both biological and machine learning viewpoints. We describe a direct constructive method of realising finite state input-dependent computations on an arbitrary directed graph. The constructed system has an excitable network attractor whose dynamics we illustrate with a number of examples. The resulting CTRNN has intermittent dynamics: trajectories spend long periods of time close to steady-state, with rapid transitions between states. Depending on parameters, transitions between states can either be excitable (inputs or noise needs to exceed a threshold to induce the transition), or spontaneous (transitions occur without input or noise). In the excitable case, we show the threshold for excitability can be made arbitrarily sensitive.
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