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Machine Learning - Connectionist approaches to language learning
The Induction of Dynamical Recognizers
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The computational brain
Sequential behavior and learning in evolved dynamical neural networks
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Integrating reactive, sequential, and learning behavior using dynamical neural networks
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Autonomous Robots
Evolution of Plastic Control Networks
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Artificial Evolution: A Continuing SAGA
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Associative learning in evolved dynamical neural networks
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Computation: finite and infinite machines
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The Dynamics of Associative Learning in Evolved Model Circuits
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Finite state automata and simple recurrent networks
Neural Computation
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Guiding for associative learning: how to shape artificial dynamic cognition
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Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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This article extends previous work on evolving learning without synaptic plasticity from discrete tasks to continuous tasks. Continuous-time recurrent neural networks without synaptic plasticity are artificially evolved on an associative learning task. The task consists in associating paired stimuli: temperature and food. The temperature to be associated can be either drawn from a discrete set or allowed to range over a continuum of values. We address two questions: Can the learning without synaptic plasticity approach be extended to continuous tasks? And if so, how does learning without synaptic plasticity work in the evolved circuits? Analysis of the most successful circuits to learn discrete stimuli reveal finite state machine (FSM) like internal dynamics. However, when the task is modified to require learning stimuli on the full continuum range, it is not possible to extract a FSM from the internal dynamics. In this case, a continuous state machine is extracted instead.