Forschungsinteressen

I have been educated and am working at the intersection between psychology, brain and data sciences. In my experimental research, I apply computational and statistical modelling techniques (e.g., machine learning) to neural recordings to illuminate mechanisms that allow humans to be remarkably flexible and robust, at unparalleled energy efficiency. Inspired by complex systems research, I leverage the power of signal analysis to improve the measurement of neural dynamics from composite brain signals. I extend available analysis methods via scientific software development, and enjoy to ponder both about mind-brain philosophy, and how neuro-computational insights can inform generalized AI development.


Dynamic characterization of neural rhythms. Neural rhythms provide insights into how the human brain coordinates information processing in time and space. However, rhythms are not constantly present in neural recordings and a major goal is to identify rhythmic periods in time to better characterize these rhythmic signals and unlock insights into their generation and function.


Characterization of ‘neural complexity’. Neural mechanisms dynamically interact across multiple temporal and spatial scales, both within and across brain regions. This gives rise to a plethora of signatures measured at the scalp. A major goal is to better characterize these fluctuations to infer the presence of different neural activity regimes.


Facing environmental uncertainty. Humans frequently face complex environments with varying degrees of uncertainty about what should receive priority in processing. A major interest of mine concerns how the brain identifies this uncertainty, and how it changes its dynamics to create an adaptive course of action.


Thalamic influences on cortical dynamics and cognition. The deep brain thalamus is ideally suited to dynamically regulate specific computations in cortex. However, its multifaceted influence on the dynamics of cortical networks in service of perception, cognition and action remains elusive. I use a multi-modal approach combining high temporal resolution in the cortical EEG with high spatial resolution fMRI to probe these relations.

Vita

B. Sc. Psychologie, Freie Universität Berlin,  2014

M. Sc. Mind and Brain - Track Brain, Humboldt-Universität zu Berlin,  2016

2016-2020 Doktorand in der IMPRS COMP2PSYCH

(https://www.mps-ucl-centre.mpg.de/en/comp2psych)


Ausgewählte Publikationen

Kosciessa, J. Q. (2020). Measurement and relevance of rhythmic and aperiodic human brain dynamics. (Dr. rer. nat.). Humboldt-Universität zu Berlin. doi:10.18452/22040 

Kosciessa, J. Q., Grandy, T. H., Garrett, D. D., & Werkle-Bergner, M. (2020). Single-trial characterization of neural rhythms: Potential and challenges. NeuroImage, 206, 116331. doi:10.1016/j.neuroimage.2019.116331

Kosciessa, J. Q., Kloosterman, N. A., & Garrett, D. D. (2020). Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it? PLoS Computational Biology, 16(5), e1007885. doi:10.1371/journal.pcbi.1007885

Kosciessa, J. Q., Lindenberger, U., & Garrett, D. D. (2020). Thalamocortical excitability adjustments guide human perception under uncertainty. bioRxiv. doi:10.1101/2020.06.22.165118

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