| SORN: a self-organizing recurrent neural network A Lazar, G Pipa, J Triesch Frontiers in computational neuroscience 3, 800, 2009 | 300 | 2009 |
| Untangling perceptual memory: Hysteresis and adaptation map into separate cortical networks CM Schwiedrzik, CC Ruff, A Lazar, FC Leitner, W Singer, L Melloni Cerebral Cortex 24 (5), 1152-1164, 2014 | 126 | 2014 |
| Fading memory and time series prediction in recurrent networks with different forms of plasticity A Lazar, G Pipa, J Triesch Neural Networks 20 (3), 312-322, 2007 | 100 | 2007 |
| Where’s the noise? Key features of spontaneous activity and neural variability arise through learning in a deterministic network C Hartmann, A Lazar, B Nessler, J Triesch PLoS computational biology 11 (12), e1004640, 2015 | 67 | 2015 |
| Stimulus complexity shapes response correlations in primary visual cortex M Bányai, A Lazar, L Klein, J Klon-Lipok, M Stippinger, W Singer, G Orbán Proceedings of the National Academy of Sciences 116 (7), 2723-2732, 2019 | 53 | 2019 |
| Does the cerebral cortex exploit high-dimensional, non-linear dynamics for information processing? W Singer, A Lazar Frontiers in computational neuroscience 10, 99, 2016 | 45 | 2016 |
| Predictive coding of natural images by V1 firing rates and rhythmic synchronization C Uran, A Peter, A Lazar, W Barnes, J Klon-Lipok, KA Shapcott, R Roese, ... Neuron 110 (7), 1240-1257. e8, 2022 | 44 | 2022 |
| Cortical gamma-band resonance preferentially transmits coherent input CM Lewis, J Ni, T Wunderle, P Jendritza, A Lazar, I Diester, P Fries Cell reports 35 (5), 2021 | 33 | 2021 |
| Emerging Bayesian priors in a self-organizing recurrent network A Lazar, G Pipa, J Triesch Artificial Neural Networks and Machine Learning–ICANN 2011: 21st …, 2011 | 15 | 2011 |
| Visual exposure enhances stimulus encoding and persistence in primary cortex A Lazar, C Lewis, P Fries, W Singer, D Nikolic Proceedings of the National Academy of Sciences 118 (43), e2105276118, 2021 | 12 | 2021 |
| Predictability in natural images determines V1 firing rates and synchronization: A deep neural network approach C Uran, A Peter, A Lazar, W Barnes, J Klon-Lipok, KA Shapcott, R Roese, ... bioRxiv, 2020.08. 10.242958, 2020 | 11* | 2020 |
| Orienting towards ensembles: from single cells to neural populations CM Lewis, AE Lazar Journal of Neuroscience 33 (1), 2-3, 2013 | 9 | 2013 |
| Reward-related suppression of neural activity in macaque visual area v4 KA Shapcott, JT Schmiedt, K Kouroupaki, R Kienitz, A Lazar, W Singer, ... Cerebral Cortex 30 (9), 4871-4881, 2020 | 7 | 2020 |
| Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex Y Yiling, K Shapcott, A Peter, J Klon-Lipok, H Xuhui, A Lazar, W Singer Nature Communications 14 (1), 3021, 2023 | 6 | 2023 |
| Visual exposure optimizes stimulus encoding in primary visual cortex A Lazar, C Lewis, P Fries, W Singer, D Nikolic bioRxiv, 502328, 2018 | 4 | 2018 |
| Stimulus content shapes cortical response statistics M Bányai, A Lazar, L Klein, J Klon-Lipok, W Singer, G Orbán bioRxiv, 238774, 2017 | 3 | 2017 |
| Key features of neural variability emerge from self-organized sequence learning in a deterministic neural network C Hartmann, A Lazar, J Triesch BMC Neuroscience 16 (Suppl 1), P266, 2015 | 3 | 2015 |
| Where’s the noise? Key features of neuronal variability and inference emerge from self-organized learning C Hartmann, A Lazar, J Triesch bioRxiv, 011296, 2014 | 3 | 2014 |
| Spontaneous activity in a self-organizing recurrent network reflects prior learning A Lazar, G Pipa, J Triesch Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience, 2010 | 3 | 2010 |
| Predictive coding in cortical microcircuits A Lazar, G Pipa, J Triesch Artificial Neural Networks-ICANN 2008: 18th International Conference, Prague …, 2008 | 3 | 2008 |
Wolf SingerProfessor für Neuroscience, Max Planck InstituteEmail verificata su brain.mpg.de