Google Scholar Profile is here
Complete List of Publications: †=(VCU) Undergraduate Student, *=(VCU) Graduate Student.
• A. Barreiro, A.J. Fontenele, C. Ly, P. Raju* , S.H. Gautam, W. Shew, 2024. Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces. Proceedings of the National Academy of Sciences Nexus (1) Vol. 3: pp 1-10 [BibTex] [pdf]
• M.F. Craft* , A. Barreiro, S.H. Gautam, W. Shew, C. Ly, 2023. Odor modality is transmitted to cortical brain regions from the olfactory bulb. Journal of Neurophysiology (5) Vol. 130: pp 1226-1242 [BibTex] [pdf]
• M.L. Brown* , C. Ly, 2023. Estimating the correlation between operational risk loss categories over different time horizons. Journal of Operational Risk (4) Vol. 18: pp 1-31 [BibTex] [pdf]
• M.F. Craft* & C. Ly, 2022. The effects of background noise on a biophysical model of olfactory bulb mitral cells. Bulletin of Mathematical Biology (107) Vol. 84: pp 1--20. [BibTex] [pdf]
• C. Ly & S. Weinberg, 2022. Automaticity in ventricular myocyte cell pairs with ephaptic and gap junction coupling. Chaos (3) Vol. 32: pp 033123. [BibTex] [pdf]
• M.F. Craft* , A. Barreiro, S.H. Gautam, W. Shew, C. Ly, 2021. Differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation revealed by data-driven models. PLoS Computational Biology (9), Vol. 17: pp e1009169. [BibTex]
• C. Ly, A. Barreiro, S.H. Gautam, W. Shew, 2021. Odor-evoked increases in olfactory bulb mitral cell spiking variability. iScience, Vol. 24: pp 102946, [BibTex]
• E. Enkhtaivan*, J. Nishimura, C. Ly, A.L. Cochran, 2021. A Competition of Critics in Human Decision-Making. computational psychiatry, Vol. 5: pp 81-101. [BibTex] [pdf]
• K. Wendling* & C. Ly, 2021. Statistical Analysis of Decoding Performances of Diverse Populations of Neurons. Neural Computation, Vol. 33: pp 764-801. [BibTex] [pdf]
• C. Ly, W. Shew, A. Barreiro, 2019. Efficient calculation of heterogeneous non-equilibrium statistics in coupled firing-rate models. Journal of Mathematical Neuroscience, Vol. 9: pp 2. [BibTex] [pdf]
• C.A. Reynolds, D.S. O'Leary, C. Ly, S.A. Smith, Z. Minic, 2019. Development of a decerebrate model for investigating mechanisms mediating viscero-sympathetic reflexes in the spinalized rat. American Journal of Physiology-Heart and Circulatory Physiology, Vol. 316: pp H1332-H1340. [BibTex] [pdf]
• K. Wendling* & C. Ly, 2019. Firing Rate Distributions in a Feedforward Network of Neural Oscillators with Intrinsic and Network Heterogeneity. Mathematical Biosciences and Engineering, Vol. 16: pp 2023-2048. [BibTex] [pdf]
• C. Ly & S. Weinberg, 2018. Analysis of Heterogeneous Cardiac Pacemaker Tissue Models and Traveling Wave Dynamics. Journal of Theoretical Biology, Vol. 459: pp 18-35. [BibTex] [pdf]
• A. Barreiro & C. Ly, 2018. Investigating the correlation-firing rate relationship in heterogeneous recurrent networks. Journal of Mathematical Neuroscience, Vol. 8: pp 8. [BibTex] [pdf]
• C. Ly & G. Marsat, 2018. Variable Synaptic Strengths Controls the Firing Rate Distribution in Feedforward Neural Networks. Journal of Computational Neuroscience, Vol. 44: pp 75-95. [BibTex] [pdf]
• A. Barreiro, S.H. Gautam, W. Shew, C. Ly, 2017. A Theoretical Framework for Analyzing Coupled Neuronal Networks: Application to the Olfactory System. PLoS Computational Biology (10), Vol. 13: pp e1005780. [BibTex] [pdf]
• A. Barreiro & C. Ly, 2017. Practical approximation method for firing rate models of coupled neural networks with correlated inputs. Physical Review E (2), Vol. 96: pp 022413. [BibTex] [pdf]
• C. Ly & B. Doiron, 2017. Noise-Enhanced Coding in Phasic Neuron Spike Trains. PLoS ONE (5), Vol. 12: pp e0176963. [BibTex] [pdf]
• A. Barreiro & C. Ly, 2017. When do Correlations Increase with Firing Rate in Recurrent Networks? PLoS Computational Biology (4), Vol. 13: pp. e1005506. [BibTex] [pdf]
• L. Crow†. 2016. Realistic spiking neuron statistics in a population are described by a single parametric distribution. Sponsor: C. Ly. SIAM Undergraduate Research Online (SIURO), Vol. 9: pp. 41-55.
• C. Ly, 2015. Firing Rate Dynamics in Recurrent Spiking Neural Networks with Intrinsic and Network Heterogeneity. Journal of Computational Neuroscience, Vol. 39: pp. 311-327. [BibTex] [pdf]
• W. Nicola*, C. Ly, S.A. Campbell, 2015. One-Dimensional Population Density Approaches to Recurrently Coupled Networks of Neurons with Noise. SIAM Journal on Applied Mathematics, Vol. 75: pp. 2333-2360. [BibTex] [pdf]
• C. Ly, 2014. Dynamics of Coupled Noisy Neural Oscillators with Heterogeneous Phase Resetting Curves. SIAM Journal on Applied Dynamical Systems, Vol. 13: pp. 1733--1755. [BibTex] [pdf]
• C. Ly, 2013. A Principled Dimension-Reduction Method for the Population Density Approach to Modeling Networks of Neurons with Synaptic Dynamics. Neural Computation, Vol. 25: pp. 2682-2708. [BibTex] [pdf]
• C. Ly, J.W. Middleon, B. Doiron, 2012. Cellular and circuit mechanisms maintain low spike co-variability and enhance population coding in somatosensory cortex. Frontiers in Computational Neuroscience, Vol. 6, Article 7: pp. 1-26. doi:10.3389/fncom.2012.00007. [BibTex] [pdf]
• C. Ly & B. Ermentrout, 2011. Analytic Approximations of Statistical Quantities and Response of Noisy Oscillators. Physica D, Vol. 240: pp. 719-731. [BibTex] [pdf]
• C. Ly, T. Melman†, A.L. Barth, & B. Ermentrout, 2011. Phase-resetting Curve Determines how BK Currents Effect Neuronal Firing. Journal of Computational Neuroscience, Vol. 30: pp. 211-223. [BibTex] [pdf]
• C. Ly & B. Ermentrout, 2010. Coupling Regularizes Individual Units in Noisy Populations. Physical Review E, Vol. 81: pp. 011911. [BibTex] [pdf]
• C. Ly & B. Ermentrout, 2010. Analysis of Recurrent Networks of Pulse-Coupled Noisy Neural Oscillators. SIAM Journal on Applied Dynamical Systems, Vol. 9: pp. 113-137. [BibTex] [pdf]
• C. Ly & B. Doiron, 2009. Divisive Gain Modulation with Dynamic Stimuli in Integrate-and-fire Neurons. PLoS Computational Biology 5(4): e1000365. [BibTex] [pdf]
• C. Ly & B. Ermentrout, 2009. Synchronization Dynamics of Two Coupled Neural Oscillators Receiving Shared and Unshared Noisy Stimuli. Journal of Computational Neuroscience, Vol. 26: pp. 425-443. [BibTex] [pdf]
• C. Ly & D. Tranchina, 2009. Spike Train Statistics and Dynamics with Synaptic Input from any Renewal Process: A Population Density Approach. Neural Computation, Vol. 21: pp. 360-396. [BibTex] [pdf]
• C. Ly & D. Tranchina, 2007. Critical Analysis of Dimension Reduction for a Moment Closure Method in a Population Density Approach to Neural Network Modeling. Neural Computation, Vol. 19: pp. 2032-2092. [BibTex] [pdf]
• F. Apfaltrer, C. Ly, & D. Tranchina, 2006. Population density methods for stochastic neurons with realistic synaptic kinetics: Firing rate dynamics and fast computational methods. Network: Computation in Neural Systems, Vol. 17: pp. 373-419. [BibTex] [pdf]
• R. Caflisch & C. Ly, 2001. Analytic Model for Electron Confinement in a Layered Material. UCLA CAM Report. [pdf]