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

Zeraati, R., Engel, TA., Levina, A. (2022) A flexible Bayesian framework for unbiased estimation of timescales https://www.nature.com/articles/s43588-022-00214-3, Nature Computational Science 2 (3), 193-204
N Sukenik*,O Vinogradov*,E Weinreb, M Segal, A Levina** & E Moses** (2021) Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers https://doi.org/10.1073/pnas.2018459118. /Proceedings of the National Academy of Sciences of the United States of America 118 /(12),
J Zierenberg, J Wilting, V Priesemann, A Levina (2020). Tailored ensembles of neural networks optimize sensitivity to stimulus statistics https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.013115. /Physical Review Research,/ 2 (1).
Fardet, T., & Levina, A. (2020). Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions https://doi.org/10.1371/journal.pcbi.1008503. /PLoS computational biology/, /16 /(12).
A Levina, V Priesemann (2017). Subsampling scaling https://www.nature.com/articles/ncomms15140?origin=ppub. /Nature communications/, 8 (1), 1-9