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