Liam Paninski
Columbia Univesrity

Combining biophysical and statistical methods for understanding neural codes

Tuesday 23rd of October 2007 at 12:00pm
508-20 Evans Hall

The neural coding problem --- deciding which stimuli will cause a given neuron to spike, and with what probability --- is a fundamental question in systems neuroscience. The high dimensionality of both stimuli and spike trains has spurred the development of a number of sophisticated statistical techniques for learning the neural code from finite experimental data. In particular, modeling approaches based on maximum likelihood have proven to be flexible and powerful. We present three such applications here. One common thread is that the models we have chosen for these data each have concave loglikelihood surfaces, permitting tractable fitting (by maximizing the loglikelihood) even in high dimensional parameter spaces, since no local maxima can exist for the optimizer to get 'stuck' in. First we describe neural encoding models in which a linear stimulus filtering stage is followed by a noisy integrate-and-fire spike generation mechanism incorporating after-spike currents and spike-dependent conductance modulations. This model provides a biophysically more realistic alternative to models based on Poisson (memoryless) spike generation, and can effectively reproduce a variety of spiking behaviors. We use this model to analyze extracellular data from populations of retinal ganglion cells, simultaneously recorded during stimulation with dynamic light stimuli. Here the model provides insight into the biophysical factors underlying the reliability of these neurons' spiking responses, and provides a framework for analyzing the cross-correlations observed between these cells. (Joint work with E.J. Chichilnisky, J. Pillow, J. Shlens, E. Simoncelli, and V. Uzzell, at NYU and Salk.) Next we describe how to use this model to 'decode' the underlying subthreshold somatic voltage dynamics, given only the superthreshold spike train. We also point out some connections to spike-triggered averaging techniques. We close by discussing recent extensions to highly biophysically-detailed, conductance-based models, which have the potential to allow us to estimate the density of active channels in a cell's membrane and also to decode the synaptic input to the cell as a function of time. (With M. Ahrens, Q. Huys, and J. Vogelstein, at Gatsby and Johns Hopkins.)

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