The central theme of my lab is to discover the principles of neuronal dynamics. To this end, we use models of different complexity and multiple data-analysis technics. We believe in a substantial contribution of self-organization to defining the neural dynamics. We are aiming at uncovering how different constraints shape this self-organization.
- Excitatory/Inhibitory networks
- Computation close to criticality
- State-dependant neural computations
- Neural constraints and self-organization
- Networks structure and subsampling
- Collective dynamics and emergence
- Zierenberg, J., Wilting, J., Priesemann, V., & Levina, A. (2020). Tailored ensembles of neural networks optimize sensitivity to stimulus statistics. Physical Review Research, 2(1), 013115.
- Shi, D., Levina, A., & Noori, H. R. (2019). Refined parcellation of the nervous system by algorithmic detection of hidden features within communities. Physical Review E, 100(1), 1–14.
- Das, A., & Levina, A. (2019). Critical Neuronal Models with Relaxed Timescale Separation. Physical Review X, 9(2), 21062.
- Levina, A., & Priesemann, V. (2017). Subsampling scaling. Nature Communications, 8, 15140.
- Effenberger, F., Jost, J., & Levina, A. (2015). Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity. PLoS Computational Biology, 11(9), 1–30.
- Nagler, J., Levina, A., & Timme, M. (2011). Impact of single links in competitive percolation. Nature Physics, 7(3), 265–270.
- Levina, A., Herrmann, J. M., & Geisel, T. (2007). Dynamical synapses causing self-organized criticality in neural networks. Nature Physics, 3(12), 9.
Self-Organization and Optimality in Neural Networks
Werner Reichardt Centre for Integrative Neuroscience
Bernstein Center for Computational Neuroscience
Computer Science Department
Maria-von-Linden Str. 6
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