Optimal decoding of stimulus velocity using a probabilistic model
of ganglion cell populations in primate retina.
JOSA A (special issue on ideal observers and efficiency) 26: B25-42
A major open problem in systems neuroscience is to understand the
relationship between behavior and the detailed spiking properties of
neural populations. In this work, we assess how faithfully velocity
information can be decoded from a population of spiking model retinal
neurons whose spatiotemporal receptive fields and ensemble spike-train
dynamics are closely matched to real data. We describe how to compute
the optimal Bayesian estimate of image velocity given the population
spike train response, and show that, given complete information about
the displayed image, the spike train ensemble signals speed with an
average relative precision of about 2% across a specific set of
stimulus conditions. We further show how to compute the Bayesian
velocity estimate in the case where we only have some a priori
information about the (naturalistic) correlation structure of the
image, but do not know the image explicitly. As expected, the
performance of the Bayesian decoder is shown to be less accurate with
decreasing prior image information. There turns out to be a close
mathematical connection between a biologically-plausible "motion
energy" method for decoding the velocity and the optimal Bayesian
decoder in the case that the image is not known. Simulations using the
motion energy method reveal that it results in an average relative
precision of only 10% across the same set of stimulus
conditions. Estimation performance is rather insensitive to the
details of the precise receptive field location, correlated activity
between cells, and spike timing.
Preprint | Liam
Paninski's research page