Research

Physics of brain network structure and function

References:

  • Christopher W. Lynn, Caroline M. Holmes, and Stephanie E. Palmer. Heavy-tailed neuronal connectivity arises from Hebbian self-organization. Submitted. | bioRxiv

  • Christopher W. Lynn and Dani S. Bassett. The physics of brain network structure, function, and control. Nature Reviews Physics (2019). | Nat Rev Phys | arXiv

  • Christopher W. Lynn, Caroline M. Holmes, and Stephanie E. Palmer. Emergent scale-free networks. Submitted. | arXiv

  • Lia Papadopoulos, Christopher W. Lynn, Demian Battaglia, and Dani S. Bassett. Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state. PLOS Computational Biology (2020). | PLOS Comput Biol | arXiv

  • Dale Zhou, Christopher W. Lynn, Zaixu Cui, Rastko Ciric, Graham L. Baum, Tyler M. More, David R. Roalf, John A. Detre, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, and Dani S. Bassett. Efficient coding in the economics of human brain connectomics. Network Neuroscience (2022). | Netw Neurosci | bioRxiv

Irreversibility and the arrow of time in the brain

References:

  • Christopher W. Lynn, Eli J. Cornblath, Lia Papadopoulos, Maxwell A. Bertolero, and Dani S. Bassett. Broken detailed balance and entropy production in the human brain. Proceedings of the National Academy of Sciences (2021). | PNAS | arXiv

  • Christopher W. Lynn, Caroline M. Holmes, William Bialek, and David J. Schwab. Decomposing the local arrow of time in interacting systems. Physical Review Letters (2022). | PRL | arXiv

  • Christopher W. Lynn, Caroline M. Holmes, William Bialek, and David J. Schwab. Emergence of local irreversibility in complex interacting systems. Physical Review E (2022). | PRE | arXiv

Human network learning and information processing

  • William Qian, Christopher W. Lynn, Andrei A. Klishin, Jennifer Stiso, Nicolas H. Christianson, and Dani S. Bassett. Optimizing the human learnability of abstract network representations. Proceedings of the National Academy of Sciences (2022). | PNAS | arXiv

  • Sophia U. David, Sophie E. Loman, Christopher W. Lynn, Ann S. Blevins, and Dani S. Bassett. How we learn about our networked world. Frontiers for Young Minds (2022). | Front Young Minds | arXiv

  • Shubhankar P. Patankar, Dale Zhou, Christopher W. Lynn, Jason Z. Kim, Harang Ju, David M. Lydon-Staley, and Dani S. Bassett. Examining theories of curiosity using knowledge networks. Submitted. | arXiv

References:

  • Christopher W. Lynn and Dani S. Bassett. Quantifying the compressibility of complex networks. Proceedings of the National Academy of Sciences (2021). | PNAS | arXiv

  • Christopher W. Lynn and Dani S. Bassett. How humans learn and represent networks. Proceedings of the National Academy of Sciences (2020). | PNAS | arXiv

  • Christopher W. Lynn, Lia Papadopoulos, Ari E. Kahn, and Dani S. Bassett. Human information processing in complex networks. Nature Physics (2020). | Nat Phys | arXiv

  • Christopher W. Lynn, Ari E. Kahn, Nathaniel Nyema, and Dani S. Bassett. Abstract representations of events arise from mental errors in learning and memory. Nature Communications (2020). | Nat Commun | arXiv

  • Jennifer Stiso, Christopher W. Lynn, Ari E. Kahn, Vinitha N. Rangarajan, Karol Szymula, Ryan Archer, Andrew Revell, Joel M. Stein, Brian Litt, Kathryn A. Davis, Timothy H. Lucas, and Dani S. Bassett. Neurophysiological evidence for cognitive map formation during sequence learning. eNeuro (2022). | eNeuro | bioRxiv

Inference and control of Ising networks

References:

  • Christopher W. Lynn, Lia Papadopoulos, Daniel D. Lee, and Dani S. Bassett. Surges of collective human activity emerge from simple pairwise correlations. Physical Review X (2019). | Phys Rev X | arXiv

  • Christopher W. Lynn and Daniel D. Lee. Maximizing Activity in Ising Networks via the TAP Approximation. In Association for the Advancement of Artificial Intelligence (2018). | AAAI | arXiv

  • Christopher W. Lynn and Daniel D. Lee. Statistical Mechanics of Influence Maximization with Thermal Noise. Europhysics Letters (2017). | EPL

  • Christopher W. Lynn and Daniel D. Lee. Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution. In Advances in Neural Information Processing Systems (2016). | NIPS | arXiv

  • Arian Ashourvan, Preya Shah, Adam Pines, Shi Gu, Christopher W. Lynn, Dani S. Bassett, Katheryn A. Davis, and Brian Litt. Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states. Communications Biology (2021). | Commun Biol | bioRxiv

Channeling radiation at Fermilab

References:

  • Tanaji Sen and Christopher Lynn. Spectral Brilliance of Channeling Radiation at the ASTA Photoinjector. Journal of Modern Physics A (2014). | Int J Mod Phys A | arXiv

  • Ben Blomberg, Daniel Mihalcea, Harsha Panuganti, Philippe Piot, Charles Brau, Bo Choi, William Gabella, Borislav Ivanov, Marcus Mendenhall, Christopher Lynn, Tanaji Sen, and Wolfgang Wagner. Planned High-Brightness Channeling Radiation Experiment at Fermilab’s Advanced Superconducting Test Accelerator. In International Particle Accelerator Conference (2014). | IPAC