| A UNIFICATION OF WEIGHTED AND UNWEIGHTED PARTICLE FILTERS | 0 | 0.34 | 2022 |
| Asymptotically Exact Unweighted Particle Filter for Manifold-Valued Hidden States and Point Process Observations. | 0 | 0.34 | 2020 |
| Bayesian regression explains how human participants handle parameter uncertainty. | 0 | 0.34 | 2020 |
| Propagation of spiking moments in linear Hawkes networks | 0 | 0.34 | 2020 |
| Online Maximum-Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes | 1 | 0.40 | 2019 |
| How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and without Importance Weights. | 2 | 0.39 | 2019 |
| Propagation of moments in Hawkes networks. | 0 | 0.34 | 2018 |
| Optimised information gathering in smartphone users. | 0 | 0.34 | 2017 |
| Quantifying the priority placed on scale-free smartphone actions. | 0 | 0.34 | 2016 |
| Sequence learning with hidden units in spiking neural networks. | 14 | 0.84 | 2011 |
| STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. | 12 | 0.81 | 2010 |
| Know Thy Neighbour: A Normative Theory of Synaptic Depression. | 2 | 0.46 | 2009 |
| Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution. | 19 | 1.57 | 2007 |
| Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning. | 81 | 4.72 | 2006 |
| Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects | 1 | 0.35 | 2005 |
| Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model | 9 | 1.02 | 2004 |
| Optimal Hebbian learning: a probabilistic point of view | 9 | 0.71 | 2003 |
