Why does in vivo and in vitro activity differ so clearly, one showing continuous activity, the other characteristic bursts? A potential explanation is derived in:
Zierenberg, Wilting & Priesemann, Homeostatic plasticity and external input shape neural network dynamics. Phys Rev X (2018)
Wilting & Priesemann, Inferring collective dynamical states from widely unobserved systems. Nature Comm. (2018)
From brains to social networks and ecosystems, most often only a tiny fraction of the system can be observed. Such spatial subsampling can incur strong biases to measures as basic as the correlation strength. We derived an approach to overcome the subsampling-induced bias, which enables to infer the stability and distance to criticality – in principle from observing a single unit.
* This article is discussed in the recent popular science article on brains near their tipping point in Quanta magazine.
Priesemann & Shriki, Can a time varying drive give rise to apparent criticality in neural systems? Plos Comp Biol. (2018)
* This article became highlighted on the Complexity Channel: “This paper shows that a quite wide family of inputs can lead to the detection of signatures of criticality in a system which is otherwise noncritical, and proposes to extend the criticality analysis beyond looking at power laws, the most used signature of criticality. These results are generalizable to other complex systems.”
Long-range correlations in human music performance based on analyses of about 100 music pieces:
Sogorski, Geisel & Priesemann, Correlated microtiming deviations in jazz and rock music. Plos ONE (2018)
An analytical treatment of subsampling effects:
Levina & Priesemann, Subsampling Scaling. Nature Comm. (2017)
Using partial information decomposition to derive neural goal functions:
Wibral, Priesemann, Kay, Lizier & Phillips, Partial information decomposition as a unified approach to the specification of neural goal functions. Brain and Cognition (2017).
A review on information theory in neuroscience:
Wibral, Lizier & Priesemann, Bits from brains for biologically inspired computing. Front. Robot. AI (2015)
First evidence for reverberating collective dynamics:
Priesemann et al., Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front. Syst. Neuroscience (2014)
Collective neural dynamics change from wakefulness to deep sleep:
Priesemann, Wibral, Valderrama & Quyen, Neuronal Avalanches Differ from Wakefulness to Deep Sleep – Evidence from Intracranial Depth Recordings in Humans. Plos Comp Biol (2013)
First report on subsampling effects in critical systems:
Priesemann, Munk & Wibral, Subsampling effects in neuronal avalanche distributions recorded in vivo. BMC Neurosci (2009)