Estimation and analysis of stochastic biophysical neural models

A major goal is to establish closer links between the spike train coding properties of neurons and the underlying biophysical computations performed "below the threshold" and in the dendrites. Recent advances in voltage- and calcium-sensitive imaging methods have brought this goal closer to reality.

Paninski, L., Pillow, J., & Simoncelli, E. (2004). Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Computation 16: 2533-2561.

Paninski, L., Pillow, J., & Simoncelli, E. (2004). Comparing integrate-and-fire-like models estimated using intracellular and extracellular data. Neurocomputing 65: 379-385.

Paninski, L. (2004). Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems 15: 243-262.

Paninski, L. (2006). The most likely voltage path and large deviations approximations for integrate-and-fire neurons. Journal of Computational Neuroscience 21: 71-87.

Paninski (2006). The spike-triggered average of the integrate-and-fire cell driven by Gaussian white noise. Neural Computation 18: 2592-2616.

Huys, Q., Ahrens, M. & Paninski, L. (2006). Efficient estimation of detailed single-neuron models. Journal of Neurophysiology 96: 872-890.

Huys, Q. & Paninski, L. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings. PLOS Computational Biology 5: e1000379.

Vogelstein, J., Watson, B., Packer, A., Yuste, R., Jedynak, B. & Paninski, L. (2009). Spike inference from calcium imaging using sequential Monte Carlo methods. Biophysical Journal 97: 636-655.

Paninski, L., Ahmadian, Y., Ferreira, D., Koyama, S., Rahnama, K., Vidne, M., Vogelstein, J. & Wu, W. (2009). A new look at state-space models for neural data. In press, Journal of Computational Neuroscience (special issue on statistical analysis of neural data).

Koyama, S. & Paninski, L. (2009). Efficient computation of the most likely path in integrate-and-fire and more general state-space models. In press, Journal of Computational Neuroscience.

Paninski, L. (2010). Fast Kalman filtering on quasilinear dendritic trees. Journal of Computational Neuroscience 28: 211-28.

Mishchenko, Y., Vogelstein, J. & Paninski, L. (2010). A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data. Annals of Applied Statistics.

Vogelstein, J., Packer, A., Machado, T., Sippy, T., Babadi, B., Yuste, R. & Paninski, L. (2010). Fast non-negative deconvolution for spike train inference from calcium imaging. J. Neurophys.

Huggins, J. & Paninski, L. (2011). Optimal experimental design for sampling voltage on dendritic trees. J. Comput. Neuro.

Liam Paninski's research