Full Publication List

  • C. Kim and D. Q. Nykamp. The influence of depolarization block on seizure-like activity in networks of excitatory and inhibitory neurons. Journal of Computational Neuroscience, 43:65-79, 2017. Pubmed, Publisher's web site
  • D. Q. Nykamp, A. Compte, D. Friedman, S. Shaker, M. Shinn, M. Vella, A. Roxin. Mean-field equations for neuronal networks with arbitrary degree distributions. Physical Review E, 95: 042323, 2017. Pubmed, Publisher's web site, Preprint
  • K. Wimmer, D. Q. Nykamp, C. Constantinidis, and A. Compte. Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory. Nature Neuroscience, 17: 431-439, 2014. Pubmed, Publisher's web site
  • N.F. Day, K.L. Terleski, D. Q. Nykamp, and T. A. Nick. Directed functional connectivity matures with motor learning in a cortical pattern generator. Journal of Neurophysiology, 109: 913-923, 2013. Pubmed, Publisher's web site
  • M. E. Koelling and D. Q. Nykamp. Searching for optimal stimuli: ascending a neuron's response function. Journal of Computational Neuroscience, 33: 449-473, 2012. Publisher's web site
  • B. Beverlin II, J. Kakalios, D. Nykamp, and T.I. Netoff. Dynamical changes in neurons during seizures determine tonic to clonic shift. Journal of Computational Neuroscience, 33: 41-51, 2012. Publisher's web site
  • L. Zhao, B. Beverlin II, T. Netoff, and D. Q. Nykamp. Synchronization from second order network connectivity statistics. Frontiers in Computational Neuroscience, 5:28, 2011. HTML/PDF, Code to generate SONETs.
  • C.-Y. Liu and D. Q. Nykamp. A kinetic theory approach to capturing interneuronal correlation: The feedforward case. Journal of Computational Neuroscience, 26: 339-368, 2009. PDF, Publisher's web site
  • D. Q. Nykamp. A stimulus-dependent connectivity analysis of neuronal networks. Journal of Mathematical Biology, 59: 147-173, 2009. PDF, Publisher's web site
  • M. E. Koelling and D. Q. Nykamp. Computing linear approximations to nonlinear neuronal response. Network: Computation in Neural Systems, 19: 286-313, 2008. PDF, Publisher's web site
  • D. Q. Nykamp. Pinpointing connectivity despite hidden nodes within stimulus-driven networks. Physical Review E, 78:021902, 2008. PDF, Publisher's web site
  • D. Q. Nykamp. Exploiting history-dependent effects to infer network connectivity. SIAM Journal on Applied Mathematics, 68:354-391, 2007. PDF, Publisher's web site
  • D. Q. Nykamp. A mathematical framework for inferring connectivity in probabilistic neuronal networks. Mathematical Biosciences, 205: 204-251, 2007. PDF, Publisher's web site
  • D. Q. Nykamp. Revealing pairwise coupling in linear-nonlinear networks. SIAM Journal on Applied Mathematics, 65:2005-2032, 2005. PDF, Publisher's web site
  • N. Wu, A. Enomoto, S. Tanaka, C.-F. Hsiao, D. Q. Nykamp, E. Izhikevich, and S. H. Chandler. Persistent sodium currents in mesencephalic V neurons participate in burst generation and control of membrane excitability. Journal of Neurophysiology, 93:2710-2722, 2005. Publisher's web site
  • D. Q. Nykamp. Measuring linear and quadratic contributions to neuronal response. Network: Computation in Neural  Systems, 14:673-702, 2003. PDF, Publisher's web site
  • D. Q. Nykamp. Reconstructing stimulus-driven neural networks from spike times. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 309-316. MIT Press, Cambridge, MA, 2003. PDF, Gzipped Postscript
  • D. Q. Nykamp. White noise analysis of coupled linear-nonlinear systems. SIAM Journal on Applied Mathematics, 63:1208-1230, 2003. PDF, Gzipped Postscript, Publisher's web site
  • D. Q. Nykamp. Spike correlation measures that eliminate stimulus effects in response to white noise. Journal of Computational Neuroscience, 14:193-209, 2003. PDF, Gzipped Postscript, Publisher's web site
  • D. Q. Nykamp and D. L. Ringach. Full identification of a linear-nonlinear system via cross-correlation analysis. Journal of Vision, 2:1-11, 2002. HTML/PDF
  • E. Haskell, D. Q. Nykamp, and D. Tranchina. Population density methods for large-scale modeling of neuronal networks with realistic synaptic kinetics: Cutting the dimension down to size. Network: Computation in Neural Systems, 12:141-174, 2001. PDF, Publisher's web site
  • D. Q. Nykamp and D. Tranchina. A population density approach that facilitates large-scale modeling of neural networks: Extension to slow inhibitory synapses. Neural Computation, 13:511-546, 2001. PDF, Gzipped Postscript, Publisher's web site
  • D. Q. Nykamp and D. Tranchina. Fast neural network simulations with population density methods. Neurocomputing. 32:487-492, 2000. PDF, Gzipped Postscript
  • D. Q. Nykamp and D. Tranchina. A population density approach that facilitates large-scale modeling of neural networks: analysis and an application to orientation tuning. Journal of Computational Neuroscience, 8:19-50, 2000. PDF, Gzipped Postscript, Publisher's web site