Measuring the Accuracy of the Neural Code

Tuning curves are commonly used by the neuroscience community to characterise the response properties of sensory neurons to external stimuli. However, the interpretation of tuning curves remains an issue of debate. Do neurons most accurately encode stimuli located at the peak of their tuning curve, where they elicit maximal ring rates, and thus the response is most distinctive against background noise? Or do neurons most accurately encode stimuli in the high slope regions of their tuning curves, where small changes in the stimulus a ect the greatest change in response? Previous measures of encoding accuracy have either explicitly or implicitly assumed one of these two intuitions. Butts and Goldman (2006) [10] recently applied a new measure of encoding accuracy, the SSI, to the tuning curves of single neurons and a population of four neurons. The SSI predicts how the location of high encoding accuracy will shift from slope to peak regions of the tuning curve, dependent upon the level of neuronal variability and task speci city. Butts and Goldman (2006) stated that the ...SSI is computationally constrained to small populations of neurons ([10] p.0644) and did not apply their measure for populations with more than four neurons. By utilising Monte Carlo integration techniques, this project presents a novel method to apply the SSI to larger populations of neurons (up to 200), new noise regimes and smaller temporal windows. The results obtained from the application of the SSI to populations of identical Gaussian tuning curves, uniformly arrayed across the stimulus space and with uncorrelated noise, are compared to the predictions of the Fisher information (FI). The results show that the integration time window and the population size also de ne where stimuli are most accurately encoded. As predicted in the in nite limit of population size, the SSI and FI measures were seen to converge, however convergence was extremely rapid, in populations of 50 neurons the FI and SSI measures were qualitatively identical. Furthermore it is shown for the population sizes for which these measures di er (approximately 4 to 30 neurons dependent on the neural context), that the FI is not a reliable measure of encoding accuracy.