Peer Reviewed, Original Research:

C. Ly & S. Weinberg, 2018. Analysis of Heterogeneous Cardiac Pacemaker Network Models and Traveling Wave Dynamics (submitted)

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]

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]

Undergraduate Research Paper:

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.

Technical Report:

Analytic Model for Electron Confinement in a Layered Material. UCLA CAM Report. [pdf]