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Dane S. Corneil

Dane Sterling Corneil

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affiliation: EPFL, Lausanne, Laboratory of Computational Neuroscience

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2010 – 2019

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2019
[i2]
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  - journals/corr/abs-1910-10559
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Roman Pogodin, Dane S. Corneil, Alexander Seeholzer, Joseph Heng, Wulfram Gerstner:
Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks. CoRR abs/1910.10559 (2019)
2018
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  - conf/icml/CorneilGB18
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Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. ICML 2018: 1057-1066
[i1]
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  - journals/corr/abs-1802-04325
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Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation. CoRR abs/1802.04325 (2018)
2015
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  - conf/nips/CorneilG15
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Dane S. Corneil, Wulfram Gerstner:
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze-like Environments. NIPS 2015: 1684-1692
2012
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  - conf/ijcnn/CorneilSNCCID12
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Dane S. Corneil, Daniel Sonnleithner, Emre Neftci, Elisabetta Chicca, Matthew Cook, Giacomo Indiveri, Rodney J. Douglas:
Function approximation with uncertainty propagation in a VLSI spiking neural network. IJCNN 2012: 1-7
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Dane S. Corneil, Daniel Sonnleithner, Emre Neftci, Elisabetta Chicca, Matthew Cook, Giacomo Indiveri, Rodney J. Douglas:
Real-time inference in a VLSI spiking neural network. ISCAS 2012: 2425-2428

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