Christopher Lynn
Assistant ProfessorCards
About
Research
Publications
2025
Minimax entropy: The statistical physics of optimal models
Carcamo D, Weaver N, Dixit P, Lynn C. Minimax entropy: The statistical physics of optimal models. Physical Review E 2025, 112: 061001. PMID: 41560230, DOI: 10.1103/kr9x-q59y.Peer-Reviewed Original ResearchOptimal compressionStatistical physicsMinimax entropy principleHigh-dimensional datasetsDescription length principleModels of biological networksMachine learningOptimal featuresNaive implementationOptimization modelMaximum entropy modelMinimum entropyLarge-scale experimentsComputational techniquesEntropy principleEntropy modelBiological networksFeaturesDatasetNetworkStatistical physics of large-scale neural activity with loops
Carcamo D, Lynn C. Statistical physics of large-scale neural activity with loops. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2426926122. PMID: 41060767, PMCID: PMC12541312, DOI: 10.1073/pnas.2426926122.Peer-Reviewed Original ResearchConceptsStatistical physicsLarge-scale neural activityMouse visual systemOptimal networkLoop-free networksInformation-theoretic measuresPhysicsVisual systemNetworkNeural populationsVisual stimulationPopulation activityInformation-providingBiological networksNeural activityLoopMaximum informationFeedback loopEntropyNetwork of correlationsInformationFrameworkExact minimax entropy models of large-scale neuronal activity
Lynn C, Yu Q, Pang R, Palmer S, Bialek W. Exact minimax entropy models of large-scale neuronal activity. Physical Review E 2025, 111: 054411. PMID: 40533950, DOI: 10.1103/physreve.111.054411.Peer-Reviewed Original ResearchExactly solvable statistical physics models for large neuronal populations
Lynn C, Yu Q, Pang R, Bialek W, Palmer S. Exactly solvable statistical physics models for large neuronal populations. Physical Review Research 2025, 7: l022039. DOI: 10.1103/physrevresearch.7.l022039.Peer-Reviewed Original ResearchNon-equilibrium whole-brain dynamics arise from pairwise interactions
Geli S, Lynn C, Kringelbach M, Deco G, Perl Y. Non-equilibrium whole-brain dynamics arise from pairwise interactions. Cell Reports Physical Science 2025, 6: 102464. DOI: 10.1016/j.xcrp.2025.102464.Peer-Reviewed Original ResearchNon-equilibrium dynamicsEntropy productionNon-equilibriumWhole-brain dynamicsNon-equilibrium processesPairs of brain regionsPairwise interactionsMacroscopic brain regionsWhole-brain scaleBrain dynamicsBrain regionsBrain statesEntropyBrain scaleBrain activityDynamicsComplex systemsHuman brainOrderInteractionDependenceRegionBrainScaleState
2024
Publisher Correction: Heavy-tailed neuronal connectivity arises from Hebbian self-organization
Lynn C, Holmes C, Palmer S. Publisher Correction: Heavy-tailed neuronal connectivity arises from Hebbian self-organization. Nature Physics 2024, 21: 486-486. DOI: 10.1038/s41567-024-02748-x.Peer-Reviewed Original ResearchIs stochastic thermodynamics the key to understanding the energy costs of computation?
Wolpert D, Korbel J, Lynn C, Tasnim F, Grochow J, Kardeş G, Aimone J, Balasubramanian V, De Giuli E, Doty D, Freitas N, Marsili M, Ouldridge T, Richa A, Riechers P, Roldán É, Rubenstein B, Toroczkai Z, Paradiso J. Is stochastic thermodynamics the key to understanding the energy costs of computation? Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2321112121. PMID: 39471216, PMCID: PMC11551414, DOI: 10.1073/pnas.2321112121.Peer-Reviewed Original ResearchProperties of physical systemsStochastic thermodynamicsThermodynamic propertiesThermodynamics of computationPhysical systemsComputational propertiesEnergy cost of computationThermal equilibriumCost of computationGlobal clockThermodynamicsComputerDigital devicesEnergy costDigital systemsDigital computerSpontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain
Rosch R, Burrows D, Lynn C, Ashourvan A. Spontaneous Brain Activity Emerges from Pairwise Interactions in the Larval Zebrafish Brain. Physical Review X 2024, 14: 031050. PMID: 39925410, PMCID: PMC7617382, DOI: 10.1103/physrevx.14.031050.Peer-Reviewed Original ResearchWhole-brain dynamicsLarval zebrafish brainWhole-brain activityLarge-scale brain dynamicsPairwise interactionsDynamical regimesSingle-neuron resolutionStructural connectivity patternsBrain dynamicsBasins of attractionRegimeTransitionNeural populationsConnectivity patternsDynamicsDynamical systemsCalcium imagingComplex dynamical systemsInteractionIndividual neuronsBrain activityNeuronal interactionsEmergent scale-free networks
Lynn C, Holmes C, Palmer S. Emergent scale-free networks. PNAS Nexus 2024, 3: pgae236. PMID: 38966012, PMCID: PMC11223655, DOI: 10.1093/pnasnexus/pgae236.Peer-Reviewed Original ResearchHuman learning of hierarchical graphs
Xia X, Klishin A, Stiso J, Lynn C, Kahn A, Caciagli L, Bassett D. Human learning of hierarchical graphs. Physical Review E 2024, 109: 044305. PMID: 37731654, PMCID: PMC10508785, DOI: 10.1103/physreve.109.044305.Peer-Reviewed Original ResearchExposed to sequencesBehavioral manifestationsSurprising effectsHierarchical graphNeural underpinningsCoarser levelReal-world networksStatistical regularitiesTransition probabilitiesMean-field predictionsHuman participantsGraph learningHuman learningGraph topologyEvent transitionsLearning timeCoarse levelHierarchical contextNetworkGraphLearningComputer simulations