Learning sequential structure in simple recurrent networks
Advances in neural information processing systems 1
Mapping part-whole hierarchies into connectionist networks
Artificial Intelligence - On connectionist symbol processing
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
Machine Learning - Connectionist approaches to language learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
The calculi of emergence: computation, dynamics and induction
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Learning finite machines with self-clustering recurrent networks
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Dynamic cell structure learns perfectly topology preserving map
Neural Computation
On the computational power of neural nets
Journal of Computer and System Sciences
Selected papers from the 5th Spanish Symposium on Pattern recognition and images analysis : advances in pattern recognition and applications: advances in pattern recognition and applications
Exploring the computational capabilities of recurrent neural networks
Exploring the computational capabilities of recurrent neural networks
Extraction of rules from discrete-time recurrent neural networks
Neural Networks
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
Analysis of dynamical recognizers
Neural Computation
Self-organizing maps
ACM Computing Surveys (CSUR)
Knowledge-based neurocomputing
Field Guide to Dynamical Recurrent Networks
Field Guide to Dynamical Recurrent Networks
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Rule Revision With Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Natural Language Grammatical Inference with Recurrent Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Simple Strategies to Encode Tree Automata in Sigmoid Recursive Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Finite-state computation in analog neural networks: steps towards biologically plausible models?
Emergent neural computational architectures based on neuroscience
Sequence Learning - Paradigms, Algorithms, and Applications
Sequence Learning - Paradigms, Algorithms, and Applications
Training Second-Order Recurrent Neural Networks using Hints
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
ICGI '94 Proceedings of the Second International Colloquium on Grammatical Inference and Applications
Recursive Neural Networks and Automata
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
The Neural Network Pushdown Automaton: Architecture, Dynamics and Training
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Architectural bias in recurrent neural networks: fractal analysis
Neural Computation
Mathematical foundations of simple recurrent networks in language processing
Mathematical foundations of simple recurrent networks in language processing
Inducing grammars from sparse data sets: a survey of algorithms and results
The Journal of Machine Learning Research
State Automata Extraction from Recurrent Neural Nets using k-Means and Fuzzy Clustering
SCCC '03 Proceedings of the XXIII International Conference of the Chilean Computer Science Society
Introduction to probabilistic automata (Computer science and applied mathematics)
Introduction to probabilistic automata (Computer science and applied mathematics)
Spatiotemporal Connectionist Networks: A Taxonomy and Review
Neural Computation
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
An incremental approach to developing intelligent neural networkcontrollers for robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks
IEEE Transactions on Fuzzy Systems
Inductive inference from noisy examples using the hybrid finite state filter
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
LSTM recurrent networks learn simple context-free and context-sensitive languages
IEEE Transactions on Neural Networks
Improving procedures for evaluation of connectionist context-free language predictors
IEEE Transactions on Neural Networks
Markovian architectural bias of recurrent neural networks
IEEE Transactions on Neural Networks
Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons
Neural Computation
The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx
Neural Computation
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
A fuzzy neural network with fuzzy impact grades
Neurocomputing
Improving Training in the Vicinity of Temporary Minima
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Computers and Industrial Engineering
Spatio-temporal memories for machine learning: a long-term memory organization
IEEE Transactions on Neural Networks
Improving rule extraction from neural networks by modifying hidden layer representations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
On the Importance of Comprehensible Classification Models for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On a hybrid weightless neural system
International Journal of Bio-Inspired Computation
Extracting reduced logic programs from artificial neural networks
Applied Intelligence
Analysis of artificial neural network learning near temporary minima: A fuzzy logic approach
Fuzzy Sets and Systems
CrySSMEx, a novel rule extractor for recurrent neural networks: overview and case study
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Efficiently explaining decisions of probabilistic RBF classification networks
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
Quality of classification explanations with PRBF
Neurocomputing
Rule extraction from ensemble methods using aggregated decision trees
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
How to design agent-based simulation models using agent learning
Proceedings of the Winter Simulation Conference
Behavior Abstraction Robustness in Agent Modeling
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning symbolic representations of hybrid dynamical systems
The Journal of Machine Learning Research
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Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the underlying RNN, typically in the form of finite state machines, that mimic the network to a satisfactory degree while having the advantage of being more transparent. RE from RNNs can be argued to allow a deeper and more profound form of analysis of RNNs than other, more or less ad hoc methods. RE may give us understanding of RNNs in the intermediate levels between quite abstract theoretical knowledge of RNNs as a class of computing devices and quantitative performance evaluations of RNN instantiations. The development of techniques for extraction of rules from RNNs has been an active field since the early 1990s. This article reviews the progress of this development and analyzes it in detail. In order to structure the survey and evaluate the techniques, a taxonomy specifically designed for this purpose has been developed. Moreover, important open research issues are identified that, if addressed properly, possibly can give the field a significant push forward.