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Título del libro: Advances In Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010
Título del capítulo: Linear readout from a neural population with partial correlation data

Autores UNAM:
RANULFO ROMO TRUJILLO;

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Autores externos:

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Idioma:

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Año de publicación:
2010

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Palabras clave:

Discrimination thresholds; Neural populations; Noise correlation; Noise correlation matrixes; Parametric dependencies; Parametric models; Partial correlation; Population sizes; Primary somatosensory cortex; Response variability; Signal correlation; Wishart distribution; Independent component analysis; Neural networks; Population statistics; Matrix algebra

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Resumen:

How much information does a neural population convey about a stimulus? Answers to this question are known to strongly depend on the correlation of response variability in neural populations. These noise correlations, however, are essentially immeasurable as the number of parameters in a noise correlation matrix grows quadratically with population size. Here, we suggest to bypass this problem by imposing a parametric model on a noise correlation matrix. Our basic assumption is that noise correlations arise due to common inputs between neurons. On average, noise correlations will therefore reflect signal correlations, which can be measured in neural populations. We suggest an explicit parametric dependency between signal and noise correlations. We show how this dependency can be used to "fill the gaps" in noise correlations matrices using an iterative application of the Wishart distribution over positive definitive matrices. We apply our method to data from the primary somatosensory cortex of monkeys performing a two-alternative-forced choice task. We compare the discrimination thresholds read out from the population of recorded neurons with the discrimination threshold of the monkey and show that our method predicts different results than simpler, average schemes of noise correlations.

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Entidades citadas de la UNAM:

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Fuente:


ISBN:
9781617823800
Editorial:

Tipo de producto:
Conference Paper