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Peer Reviewed, Original Research:


A. Barreiro & C. Ly, 2017. Practial approximation method for firing rate models of coupled neural networks with correlated inputs. Physical Review E (2), Vol. 96: pp 022413.  [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. (submitted)


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 & G. Marsat, 2017.  Variable Synaptic Strengths Controls the Firing Rate Distribution in Feedforward Neural Networks. (submitted)


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 Comput Biol 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]