| Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances | 0 | 0.34 | 2021 |
| When shared concept cells support associations: Theory of overlapping memory engrams | 0 | 0.34 | 2021 |
| Novelty Is Not Surprise: Human Exploratory And Adaptive Behavior In Sequential Decision-Making | 0 | 0.34 | 2021 |
| Optimal stimulation protocol in a bistable synaptic consolidation model. | 0 | 0.34 | 2019 |
| Non-linear motor control by local learning in spiking neural networks. | 0 | 0.34 | 2018 |
| Learning to Generate Music with BachProp. | 0 | 0.34 | 2018 |
| Balancing New against Old Information: The Role of Puzzlement Surprise in Learning. | 1 | 0.36 | 2018 |
| Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory. | 0 | 0.34 | 2018 |
| BachProp: Learning to Compose Music in Multiple Styles. | 0 | 0.34 | 2018 |
| Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons. | 4 | 0.43 | 2017 |
| Predicting non-linear dynamics: a stable local learning scheme for recurrent spiking neural networks. | 0 | 0.34 | 2017 |
| Deep Artificial Composer: A Creative Neural Network Model For Automated Melody Generation | 2 | 0.43 | 2017 |
| Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. | 11 | 0.62 | 2017 |
| Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory. | 0 | 0.34 | 2017 |
| Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons. | 5 | 0.48 | 2016 |
| Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity. | 0 | 0.34 | 2016 |
| Nonlinear Hebbian learning as a unifying principle in receptive field formation | 7 | 0.50 | 2016 |
| Algorithmic Composition of Melodies with Deep Recurrent Neural Networks. | 5 | 0.51 | 2016 |
| Automated High-Throughput Characterization Of Single Neurons By Means Of Simplified Spiking Models | 8 | 0.48 | 2015 |
| Stochastic variational learning in recurrent spiking networks. | 17 | 0.80 | 2014 |
| Spike-timing prediction in cortical neurons with active dendrites. | 1 | 0.36 | 2014 |
| Reinforcement Learning Using A Continuous Time Actor-Critic Framework With Spiking Neurons | 34 | 1.73 | 2013 |
| Synaptic Plasticity In Neural Networks Needs Homeostasis With A Fast Rate Detector | 20 | 0.93 | 2013 |
| Coding And Decoding With Adapting Neurons: A Population Approach To The Peri-Stimulus Time Histogram | 10 | 0.56 | 2012 |
| Paradoxical evidence integration in rapid decision processes. | 4 | 0.45 | 2012 |
| Improved similarity measures for small sets of spike trains. | 18 | 0.91 | 2011 |
| Variational Learning for Recurrent Spiking Networks. | 13 | 0.80 | 2011 |
| From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models. | 6 | 0.51 | 2011 |
| Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect? | 24 | 1.14 | 2011 |
| Spike-timing dependent plasticity | 12 | 1.34 | 2010 |
| STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. | 12 | 0.81 | 2010 |
| Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models | 0 | 0.34 | 2010 |
| Spike-Based Reinforcement Learning In Continuous State And Action Space: When Policy Gradient Methods Fail | 34 | 1.29 | 2009 |
| Adaptive exponential integrate-and-fire model | 7 | 0.61 | 2009 |
| Code-specific policy gradient rules for spiking neurons. | 2 | 0.39 | 2009 |
| Spike-response model | 6 | 0.51 | 2008 |
| Firing patterns in the adaptive exponential integrate-and-fire model. | 64 | 3.47 | 2008 |
| Tag-Trigger-Consolidation: A Model Of Early And Late Long-Term-Potentiation And Depression | 25 | 2.11 | 2008 |
| The quantitative single-neuron modeling competition. | 33 | 2.31 | 2008 |
| Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning | 0 | 0.34 | 2008 |
| Special issue on quantitative neuron modeling. | 3 | 0.48 | 2008 |
| Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks. | 3 | 0.43 | 2008 |
| Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves. | 15 | 0.86 | 2008 |
| Consciousness & the small network argument | 5 | 0.73 | 2007 |
| Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments | 31 | 2.43 | 2007 |
| Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution. | 19 | 1.57 | 2007 |
| An online Hebbian learning rule that performs Independent Component Analysis | 4 | 0.51 | 2007 |
| Predicting spike timing of neocortical pyramidal neurons by simple threshold models. | 83 | 4.86 | 2006 |
| Adaptive sensory processing for efficient place coding | 0 | 0.34 | 2006 |
| Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning | 0 | 0.34 | 2006 |
