Correlation susceptibility and single neuron computation
© Hong and De Schutter; licensee BioMed Central Ltd. 2008
Published: 11 July 2008
Neurons in many systems exhibit temporal correlation between their firings and it has been hypothesized that the correlations contribute to population coding . Recently, de la Rocha et al.  showed that a correlation between the spike trains of two neurons, estimated by a simple covariance measure, depends on the firing rates when the correlated noisy input current drives the neurons, in in vitro recordings and simulations. It was also theoretically derived that a ratio of an output and input correlation, termed correlation susceptibility, can be written in terms of the firing rate and its gain under some assumptions. This result revisited the question of how rate and correlation based coding would work in a neural population.
In the HH case, we show that the output correlation qualitatively follows the prediction by the firing rate and gain. However, in the HHLS case, there remains a significant correlation even when the gain of a firing rate almost vanishes, and therefore so should the prediction of de la Rocha et al. . Our results suggest that correlation susceptibility depends on the neuron's intrinsic computation and its adaptation to stimulus statistics.
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