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