Werner Reichardt Centrum für Integrative Neurowissenschaften (CIN)

Self-organization and Optimality in Neural Networks

Principles of neuronal self-organization

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. 

Main topics

  • Excitatory/Inhibitory networks
  • Computation close to criticality
  • State-dependant neural computations
  • Neural constraints and self-organization
  • Networks structure and subsampling
  • Collective dynamics and emergence

Selected Publications

  • 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.