Training deep neural density estimators to identify mechanistic models of neural dynamics PJ Gonçalves, JM Lueckmann, M Deistler, M Nonnenmacher, K Öcal, ... Elife 9, e56261, 2020 | 218 | 2020 |
Mapping the function of neuronal ion channels in model and experiment WF Podlaski, A Seeholzer, LN Groschner, G Miesenböck, R Ranjan, ... Elife 6, e22152, 2017 | 46* | 2017 |
Biological credit assignment through dynamic inversion of feedforward networks WF Podlaski, CK Machens Advances in Neural Information Processing Systems 33, 2020 | 21 | 2020 |
High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating WF Podlaski, EJ Agnes, TP Vogels bioRxiv, 2020.01. 08.898528, 2020 | 9* | 2020 |
Approximating nonlinear functions with latent boundaries in low-rank excitatory-inhibitory spiking networks WF Podlaski, CK Machens Neural Computation 36 (5), 803-857, 2024 | 4 | 2024 |
Nonlinear computations in spiking neural networks through multiplicative synapses M Nardin, JW Phillips, WF Podlaski, SW Keemink Peer Community Journal 1, 2021 | 4 | 2021 |
Storing overlapping associative memories on latent manifolds in low-rank spiking networks WF Podlaski, CK Machens arXiv preprint arXiv:2411.17485, 2024 | | 2024 |
Channels and circuits: biophysical and network models of neuronal function W Podlaski University of Oxford, 2018 | | 2018 |