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ORCIDDane S. Corneil
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- affiliation: EPFL, Lausanne, Laboratory of Computational Neuroscience
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2010 – 2019
- 2019
[i2]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
- [c4]Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient ModelBased Deep Reinforcement Learning with Variational State Tabulation. ICML 2018: 1057-1066 - [i1]Dane S. Corneil, Wulfram Gerstner, Johanni Brea:
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation. CoRR abs/1802.04325 (2018) - 2015
- [c3]Dane S. Corneil, Wulfram Gerstner:
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze-like Environments. NIPS 2015: 1684-1692 - 2012
- [c2]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 - [c1]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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