TNG Team Member

Demian Battaglia

Demian Battaglia, CR CNRS
TNG


Website:
http://www.demian-battaglia.net

Email:

Phone number:
+33 7 69 99 48 10



Short bio

After undergraduate studies at the University of Turin and a PhD at SISSA (Trieste, Italy) at the interface between statistical physics, information theory and computer science, I redirected my interest toward computational neuroscience starting in 2006 a first postdoc at the Laboratory of Neurophysics and Physiology (University Paris Descartes, CNRS UMR 8119), under the direction of David Hansel and Nicolas Brunel.

In 2009, I moved then to the Max Planck Institute for Dynamics and Selforganization (Göttingen, Germany), working among others with Theo Geisel and Fred Wolf. Since May 2010, I am Principal investigator in the local Bernstein Center for Computational Neuroscience, directing a project about modeling and data-analysis of flexible inter-circuit interactions.

Since September 2013, I integrated the Institute for Systems Neuroscience at University Aix-Marseille, first as Marie Curie Fellow and then as CNRS faculty Research Scientist, working to extend at the brain-wide scale my previous investigations on the interplay between structure, function and emergent dynamics. 

Current research topics

FUNCTIONAL CONNECTIVITY DYNAMICS - Structural connectivity constrains brain dynamics but does not fully determine it. As a matter of fact even very simple structural motifs can give rise to a multiplicity of dynamical states, each one of them associated to a different pattern of functional interactions between system's components. The noise-driven sampling of these states is thus expected to give rise to a characteristic switching non-stationarity of functional connectivity over time. We probe such switching Functional Connectivity Dynamics (FCD) in neural circuits at different scales monitoring their activity with different recording or imaging modalities, from micro- and meso-scale multichannel recordings in rodents and primates to large-scale human brain imaging. We are interested in developing a statistical characterization of spatio-temporal FCD patterns which may lead to new biomarkers of, e.g., aging, epilepsy, etc. We are also attempting to reproduce FCD in computational models informed by realistic connectivity, in order to better understand its underlying brain dynamics determinants.  FCD
EdgeSync DYNAMIC INFORMATION ROUTING AND INTEGRATION - We are interested in probing through information theory and machine-learning approaches the way in which information is encoded and routed via coordinated patterns of distributed neural activity. From a theoretical perspective, we have focused on the role played by collective oscillations in the directed sharing and transfer of information, analyzing models at different levels of abstraction, from modular networks of phase oscillators to realistic spiking simulations of networks at the edge of synchrony. We have developed tecnhiques such as state-resolved Transfer Entropy which we are currently applying to the analysis of actual data-sets. We are also developing large-scale models of brain-wide multi-frequency coherence networks, based on realistic local circuit and inter-areal connectivity information, aiming at exploring their involvement in attention or working memory.
ALGORITHMS FOR NEURAL DATA ANALYSIS AND UNSUPERVISED CLASSIFICATION - We are interested in developing advanced algorithmic methods for different types of neural data analyses. Applications have included so far: the inference of connectivity from calcium imaging of the activity of neuronal cultures; the model-free characterization of irregular tuned responses and of their modulation by attention; or, yet, soft clustering approaches for the unsupervised classification of neuronal types (and even primate vocalizations!). FuzzyClustering

Recent publications

1. Helmer M, Schottdorf M, Neef A, Battaglia D. Gender bias in scholarly peer review. eLife. 2017; 6: 6:e21718. doi: 10.7554/eLife.21718

2. Kirst C, Timme M, Battaglia D. Dynamic information routing in complex networks. Nature Communications. 2016; 7: 11061. doi: 10.1038/ncomms11061

3. Hansen ECA, Battaglia D, Spiegler A, Deco G, Jirsa VK. Functional connectivity dynamics: modeling the switching behavior of the resting state. NeuroImage. 2015;105: 525–535. doi:10.1016/j.neuroimage.2014.11.001

4. Wadewitz P, Hammerschmidt K, Battaglia D, Witt A, Wolf F, Fischer J. Characterizing Vocal Repertoires-Hard vs. Soft Classification Approaches. PLoS ONE. 2015;10: e0125785. doi:10.1371/journal.pone.0125785

5. Orlandi JG, Stetter O, Soriano J, Geisel T, Battaglia D. Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging. PLoS ONE. 2014;9: e98842. doi:10.1371/journal.pone.0098842

6. Witt A, Palmigiano A, Neef A, Hady El A, Wolf F, Battaglia D. Controlling the oscillation phase through precisely timed closed-loop optogenetic stimulation: a computational study. Front Neural Circuits. 2013;7: 49. doi:10.3389/fncir.2013.00049

7. Battaglia D, Karagiannis A, Gallopin T, Gutch HW, Cauli B. Beyond the frontiers of neuronal types. Front Neural Circuits. 2013;7: 13. doi:10.3389/fncir.2013.00013

8. Stetter O, Battaglia D, Soriano J, Geisel T. Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Comp Biol. 2012;8: e1002653. doi:10.1371/journal.pcbi.1002653

9. Battaglia D, Witt A, Wolf F, Geisel T. Dynamic Effective Connectivity of Inter-Areal Brain Circuits. PLoS Comp Biol. 2012;8: e1002438. doi:10.1371/journal.pcbi.1002438

10. Battaglia D, Hansel D. Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex. PLoS Comp Biol. 2011;7: e1002176. doi:10.1371/journal.pcbi.1002176

11. Karagiannis A, Gallopin T, Dávid C, Battaglia D, Geoffroy H, Rossier J, et al. Classification of NPY-expressing neocortical interneurons. Journal of Neuroscience. 2009;29: 3642–3659. doi:10.1523/JNEUROSCI.0058-09.2009

12. Battaglia D, Brunel N, Hansel D. Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation. Phys Rev Lett. 2007;99: 238106.