Tim Vogels

University of Oxford, Oxford, United Kingdom 
Computational neuroscience
"Tim Vogels"
Mean distance: 12.74 (cluster 6)
Cross-listing: Computational Biology Tree


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Larry F. Abbott grad student 2001-2007 Brandeis
Rafael Yuste post-doc 2007-2010 Columbia
Wulfram Gerstner post-doc 2010-2013 EPFL


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Panagiotis Bozelos research assistant 2017- Oxford
Jake Patrick Stroud grad student Oxford
William F. Podlaski grad student 2014- Oxford
Yayoi Teramoto grad student 2015-
Homero Barrocas Soares Esmeraldo grad student 2017- Oxford
Richard Lonsdale post-doc
Everton J Agnes post-doc 2015- Oxford
Chaitanya Chintaluri post-doc 2017- Oxford
Friedemann Zenke post-doc 2017-
Rui Ponte Costa post-doc 2014-2017 Oxford


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Alexander Seeholzer collaborator 2014-2017
BETA: Related publications


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Stroud JP, Porter MA, Hennequin G, et al. (2018) Publisher Correction: Motor primitives in space and time via targeted gain modulation in cortical networks. Nature Neuroscience
Stroud JP, Porter MA, Hennequin G, et al. (2018) Motor primitives in space and time via targeted gain modulation in cortical networks. Nature Neuroscience. 21: 1774-1783
Stroud JP, Vogels TP. (2018) Cortical Signal Propagation: Balance, Amplify, Transmit. Neuron. 98: 8-9
Costa RP, Padamsey Z, D'Amour JA, et al. (2017) Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity. Neuron. 96: 177-189.e7
Hennequin G, Agnes EJ, Vogels TP. (2017) Inhibitory Plasticity: Balance, Control, and Codependence. Annual Review of Neuroscience
Podlaski WF, Seeholzer A, Groschner LN, et al. (2017) Mapping the function of neuronal ion channels in model and experiment. Elife. 6
Barron HC, Vogels TP, Emir UE, et al. (2016) Unmasking Latent Inhibitory Connections in Human Cortex to Reveal Dormant Cortical Memories. Neuron
Araya R, Vogels TP, Yuste R. (2014) Activity-dependent dendritic spine neck changes are correlated with synaptic strength. Proceedings of the National Academy of Sciences of the United States of America. 111: E2895-904
Hennequin G, Vogels TP, Gerstner W. (2014) Optimal control of transient dynamics in balanced networks supports generation of complex movements. Neuron. 82: 1394-406
Tomm C, Avermann M, Petersen C, et al. (2014) Connection-type-specific biases make uniform random network models consistent with cortical recordings. Journal of Neurophysiology. 112: 1801-14
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