Elements of information theory
Elements of information theory
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
An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
Analysis of dynamical recognizers
Neural Computation
Neural Computation
Dynamical recognizers: real time language recognition by analog computers
Theoretical Computer Science
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx
Neural Computation
Back-propagation as reinforcement in prediction tasks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Markovian architectural bias of recurrent neural networks
IEEE Transactions on Neural Networks
Supervised learning in multilayer spiking neural networks
Neural Computation
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Simple recurrent networks (SRNs) in symbolic time-series prediction (e.g., language processing models) are frequently trained with gradient descent--based learning algorithms, notably with variants of backpropagation (BP). A major drawback for the cognitive plausibility of BP is that it is a supervised scheme in which a teacher has to provide a fully specified target answer. Yet agents in natural environments often receive summary feedback about the degree of success or failure only, a view adopted in reinforcement learning schemes. In this work, we show that for SRNs in prediction tasks for which there is a probability interpretation of the network's output vector, Elman BP can be reimplemented as a reinforcement learning scheme for which the expected weight updates agree with the ones from traditional Elman BP. Network simulations on formal languages corroborate this result and show that the learning behaviors of Elman backpropagation and its reinforcement variant are very similar also in online learning tasks.