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Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.
Byron M Yu, John P Cunningham, Gopal Santhanam, Stephen I Ryu, Krishna V Shenoy and Maneesh Sahani
In:
Advances in Neural Information Processing Systems
(2009)
MIT Press
, Cambridge, USA
.
There is a more recent version of this eprint available. Click here to view it. AbstractWe consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from tens to hundreds of
neurons on individual experimental trials. Current methods for extracting neural
trajectories involve a two-stage process: the data are first "denoised" by smoothing over time, then a static dimensionality reduction technique is applied. We first
describe extensions of the two-stage methods that allow the degree of smoothing
to be chosen in a principled way, and account for spiking variability that may vary
both across neurons and across time. We then present a novel method for extracting neural trajectories, Gaussian-process factor analysis (GPFA), which unifies
the smoothing and dimensionality reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded
simultaneously in macaque premotor and motor cortices during reach planning
and execution. By adopting a goodness-of-fit metric that measures how well the
activity of each neuron can be predicted by all other recorded neurons, we found
that GPFA provided a better characterization of the population activity than the
two-stage methods.
Available Versions of this Item- Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity.
(deposited 24 March 2009) [Currently Displayed]
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