Publication: Function approximation with uncertainty propagation in a VLSI spiking neural network
Function approximation with uncertainty propagation in a VLSI spiking neural network
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Corneil, D., Sonnleithner, D., Neftci, E., Chicca, E., Cook, M., Indiveri, G., & Douglas, R. (2012). Function approximation with uncertainty propagation in a VLSI spiking neural network. Proceedings of the International Joint Conference on Neural Networks, 1–7. https://doi.org/10.1109/IJCNN.2012.6252780
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The brain combines and integrates multiple cues to take coherent, context-dependent action using distributed, event-based computational primitives. Computational models that use these principles in software simulations of recurrently coupled spiking neural networks have been demonstrated in the past, but their implementation in hybrid analog/digital Very Large Scale Integration (VLSI) spiking neural networks remains challenging. Here, we demonstrate a distributed spiking neural network architecture comprising multiple neuromorphic VLS
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- Corneil, D
- Sonnleithner, D
- Neftci, E
- Chicca, E
- Cook, M
- Indiveri, G
- Douglas, R
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Corneil, D., Sonnleithner, D., Neftci, E., Chicca, E., Cook, M., Indiveri, G., & Douglas, R. (2012). Function approximation with uncertainty propagation in a VLSI spiking neural network. Proceedings of the International Joint Conference on Neural Networks, 1–7. https://doi.org/10.1109/IJCNN.2012.6252780
