Publication: Local structure helps learning optimized automata in recurrent neural networks
Local structure helps learning optimized automata in recurrent neural networks
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Binas, J., Indiveri, G., & Pfeiffer, M. (2015). Local structure helps learning optimized automata in recurrent neural networks. 1–7. https://doi.org/10.1109/IJCNN.2015.7280714
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Deterministic behavior can be modeled conveniently in the framework of finite automata. We present a recurrent neural network model based on biologically plausible circuit motifs that can learn deterministic transition models from given input sequences. Furthermore, we introduce simple structural constraints on the connectivity that are inspired by biology. Simulation results show that this leads to great improvements in terms of training time, and efficient use of resources in the converged system. Previous work has shown how specifi
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- Binas, J
- Indiveri, G
- Pfeiffer, M
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Binas, J., Indiveri, G., & Pfeiffer, M. (2015). Local structure helps learning optimized automata in recurrent neural networks. 1–7. https://doi.org/10.1109/IJCNN.2015.7280714
