| Untangling cross-frequency coupling in neuroscience J Aru, J Aru, V Priesemann, M Wibral, L Lana, G Pipa, W Singer, ... Current opinion in neurobiology 31, 51-61, 2015 | 327 | 2015 |
| TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy M Lindner, R Vicente, V Priesemann, M Wibral BMC neuroscience 12 (1), 119, 2011 | 193 | 2011 |
| Measuring information-transfer delays M Wibral, N Pampu, V Priesemann, F Siebenhühner, H Seiwert, ... PloS one 8 (2), e55809, 2013 | 182 | 2013 |
| Spike avalanches in vivo suggest a driven, slightly subcritical brain state V Priesemann, M Wibral, M Valderrama, R Pröpper, M Le Van Quyen, ... Frontiers in systems neuroscience 8, 108, 2014 | 174 | 2014 |
| Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions J Dehning, J Zierenberg, FP Spitzner, M Wibral, JP Neto, M Wilczek, ... Science, 2020 | 172* | 2020 |
| Neuronal Avalanches Differ from Wakefulness to Deep Sleep–Evidence from Intracranial Depth Recordings in Humans V Priesemann, M Valderrama, M Wibral, M Le Van Quyen PLOS Computational Biology 9 (3), e1002985, 2013 | 138 | 2013 |
| Subsampling effects in neuronal avalanche distributions recorded in vivo V Priesemann, M Munk, M Wibral BMC neuroscience 10 (1), 40, 2009 | 124 | 2009 |
| Bits from brains for biologically inspired computing M Wibral, JT Lizier, V Priesemann Frontiers in Robotics and AI 2, 5, 2015 | 76 | 2015 |
| Local active information storage as a tool to understand distributed neural information processing M Wibral, J Lizier, S Vögler, V Priesemann, R Galuske Frontiers in neuroinformatics 8, 1, 2014 | 72 | 2014 |
| Repetitive magnetic stimulation induces plasticity of excitatory postsynapses on proximal dendrites of cultured mouse CA1 pyramidal neurons M Lenz, S Platschek, V Priesemann, D Becker, LM Willems, U Ziemann, ... Brain Structure and Function 220 (6), 3323-3337, 2015 | 63 | 2015 |
| Inferring collective dynamical states from widely unobserved systems J Wilting, V Priesemann Nature communications 9 (1), 1-7, 2018 | 61* | 2018 |
| Partial information decomposition as a unified approach to the specification of neural goal functions M Wibral, V Priesemann, JW Kay, JT Lizier, WA Phillips Brain and cognition 112, 25-38, 2017 | 58 | 2017 |
| Subsampling scaling A Levina, V Priesemann Nature Communications 8 (15140:1-9), doi:10.1038/ncomms15140, 2017 | 52 | 2017 |
| Criticality meets learning: Criticality signatures in a self-organizing recurrent neural network B Del Papa, V Priesemann, J Triesch PloS one 12 (5), e0178683, 2017 | 37 | 2017 |
| Homeostatic plasticity and external input shape neural network dynamics J Zierenberg, J Wilting, V Priesemann Physical Review X 8 (3), 031018, 2018 | 36 | 2018 |
| Breakdown of local information processing may underlie isoflurane anesthesia effects P Wollstadt, KK Sellers, L Rudelt, V Priesemann, A Hutt, F Fröhlich, ... PLoS computational biology 13 (6), e1005511, 2017 | 28 | 2017 |
| Quantifying information modification in developing neural networks via partial information decomposition M Wibral, C Finn, P Wollstadt, JT Lizier, V Priesemann Entropy 19 (9), 494, 2017 | 25 | 2017 |
| Operating in a reverberating regime enables rapid tuning of network states to task requirements J Wilting, J Dehning, J Pinheiro Neto, L Rudelt, M Wibral, J Zierenberg, ... Frontiers in systems neuroscience 12, 55, 2018 | 21 | 2018 |
| Can a time varying external drive give rise to apparent criticality in neural systems? V Priesemann, O Shriki PLoS computational biology 14 (5), e1006081, 2018 | 21 | 2018 |
| Between perfectly critical and fully irregular: A reverberating model captures and predicts cortical spike propagation J Wilting, V Priesemann Cerebral Cortex 29 (6), 2759-2770, 2019 | 20* | 2019 |
Michael WibralProfessor for Data Driven Analysis of Biological Networks, Georg-August University, GöttingenBestätigte E-Mail-Adresse bei michael-wibral.de
