Likelihood-based methods for spike train analysis
Much of our recent work has involved statistical techniques for
analyzing neural spike trains given high-dimensional inputs (e.g.,
visual stimuli, or complex movements). Recently, we've been
interested in likelihood-based methods for modeling spike trains,
especially methods which allow us to model the detailed,
temporally-precise spiking statistics of neurons. A major focus is on
computational tractability; for example, we have emphasized models for
which the loglikelihood is a concave function, which makes
optimization and other computations much more feasible.
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. (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, L., Pillow, J. and Lewi, J. (2007). Statistical models for neural encoding,
decoding, and optimal stimulus design. (Invited review.)
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
In press, Journal of
Computational Neuroscience (special issue on statistical analysis of
Ahmadian, Y., Pillow, J. & Paninski, L. (2010).
Efficient Markov Chain Monte Carlo methods for
decoding population spike trains.
Pillow, J., Ahmadian, Y. & Paninski, L. (2010).
Model-based decoding, information estimation, and
change-point detection in multi-neuron spike
In press, Neural Computation.
We are currently focused on applying these methods to a variety of
physiological systems; see here for applications
to simultaneous population recordings in primary motor cortex in awake
behaving primates, and here for analysis of
dynamic light responses in retina.
"Spike-triggered" methods are quite popular in neural data analysis,
due to their computational convenience and relative interpretability;
there are close connections with the likelihood-based methods
Simoncelli, Paninski, Pillow & Schwartz. (2004). Characterization
of neural responses with stochastic stimuli. In The New Cognitive
Neurosciences, ed. Gazzaniga, M.
Paninski, L. (2003). Convergence properties of three
spike-triggered analysis techniques. Network: Computation in
Neural Systems 14: 437-464. (Special issue on natural scene
statistics and neural codes.) A beta version of the code described in
this paper is available here.
Paninski (2006). The
spike-triggered average of the integrate-and-fire cell driven by
Gaussian white noise. Neural Computation 18: 2592-2616.
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