Felipe Gerhard

Affiliations: 
École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 
Area:
computational neuroscience
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"Felipe Gerhard"
Mean distance: 14.76 (cluster 17)
 
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Parents

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Jochen Triesch research assistant 2007-2009 FIAS
Gordon Pipa research assistant 2008-2009 Max Planck Institute for Brain Research
Gaby Schneider research assistant 2008-2009 FIAS
Wulfram Gerstner grad student 2009- EPFL
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Publications

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Lütcke H, Gerhard F, Zenke F, et al. (2013) Inference of neuronal network spike dynamics and topology from calcium imaging data. Frontiers in Neural Circuits. 7: 201
Gerhard F, Kispersky T, Gutierrez GJ, et al. (2013) Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. Plos Computational Biology. 9: e1003138
Gerhard F, Szegletes L. (2012) Spline- and wavelet-based models of neural activity in response to natural visual stimulation. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2012: 4611-4
Naud R, Gerhard F, Mensi S, et al. (2011) Improved similarity measures for small sets of spike trains. Neural Computation. 23: 3016-69
Gerhard F, Haslinger R, Pipa G. (2011) Applying the multivariate time-rescaling theorem to neural population models. Neural Computation. 23: 1452-83
Gerhard F, Pipa G, Lima B, et al. (2011) Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect? Frontiers in Computational Neuroscience. 5: 4
Gerhard F, Gerstner W. (2010) Rescaling, thinning or complementing? on goodness-of-fit procedures for point process models and Generalized Linear Models Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010
Gerhard F, Savin C, Triesch J. (2009) A robust biologically plausible implementation of ICA-like learning Esann 2009 Proceedings, 17th European Symposium On Artificial Neural Networks - Advances in Computational Intelligence and Learning. 147-152
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