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- A. Onken, S. Grünewälder,
M. H. J. Munk, and K. Obermayer. Analyzing Short-Term Noise Dependencies
of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight
Transformation.
.
PLoS Comput Biol, 5(11):e1000577, November 2009.
(FTP PDF, 709 kb)
The brain has an enormous number of neurons that do not work alone
but in an ensemble. Yet, mostly individual neurons were measured in the past
and therefore models were restricted to independent neurons. With the advent
of new multi-electrode techniques, however, it becomes possible to measure a
great number of neurons simultaneously. As a result, models of how
populations of neurons co-vary are becoming increasingly important. Here, we
describe such a framework based on so-called copulas. Copulas allow to
separate the neural variation structure of the population from the
variability of the individual neurons. Contrary to standard models, versatile
dependence structures can be described using this approach. We explore what
additional information is provided by the detailed dependence. For simulated
neurons, we show that the variation structure of the population allows
inference of the underlying connectivity structure of the neurons. The power
of the approach is demonstrated on a memory experiment in macaque monkey. We
show that our framework describes the measurements better than the standard
models and identify possible network connections of the measured
neurons.
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