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Neural circuits for learning context-dependent associations of stimuli. (English) Zbl 1434.68533
Summary: The use of reinforcement learning combined with neural networks provides a powerful framework for solving certain tasks in engineering and cognitive science. Previous research shows that neural networks have the power to automatically extract features and learn hierarchical decision rules. In this work, we investigate reinforcement learning methods for performing a context-dependent association task using two kinds of neural network models (using continuous firing rate neurons), as well as a neural circuit gating model. The task allows examination of the ability of different models to extract hierarchical decision rules and generalize beyond the examples presented to the models in the training phase. We find that the simple neural circuit gating model, trained using response-based regulation of Hebbian associations, performs almost at the same level as a reinforcement learning algorithm combined with neural networks trained with more sophisticated back-propagation of error methods. A potential explanation is that hierarchical reasoning is the key to performance and the specific learning method is less important.
MSC:
68T07 Artificial neural networks and deep learning
68T05 Learning and adaptive systems in artificial intelligence
92B20 Neural networks for/in biological studies, artificial life and related topics
Software:
LSTM
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