Viola Priesemann
Cited by
Cited by
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
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
Measuring information-transfer delays
M Wibral, N Pampu, V Priesemann, F Siebenhühner, H Seiwert, ...
PloS one 8 (2), e55809, 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
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
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
Subsampling effects in neuronal avalanche distributions recorded in vivo
V Priesemann, M Munk, M Wibral
BMC neuroscience 10 (1), 40, 2009
Bits from brains for biologically inspired computing
M Wibral, JT Lizier, V Priesemann
Frontiers in Robotics and AI 2, 5, 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
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
Inferring collective dynamical states from widely unobserved systems
J Wilting, V Priesemann
Nature communications 9 (1), 1-7, 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
Subsampling scaling
A Levina, V Priesemann
Nature Communications 8 (15140:1-9), doi:10.1038/ncomms15140, 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
Homeostatic plasticity and external input shape neural network dynamics
J Zierenberg, J Wilting, V Priesemann
Physical Review X 8 (3), 031018, 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
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
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
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
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
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