Correlations between the activity of sensory neurons and behavior: how much do they tell us about a neuron's causality?

Abstract

How the activity of sensory neurons elicits perceptions and guides behavior is central to our understanding of the brain and is a subject of intense investigation in neuroscience. Correlations between the activity of sensory neurons and behavior have been widely observed and are sometimes used to infer how neurons are used to guide a certain behavior. This view is challenged firstly by theoretical considerations that these correlations rely on the existence of correlated noise and its structure, and secondly by recent empirical observations suggesting that such correlated noise is not a fixed network property but that it depends on various sources, and varies with a subject's mental state.

Figure 1. Influence of the structure of noise correlations on choice-probability

Left panel: Schematic of the pooling model developed by Shadlen et al. (1996) for a direction of motion discrimination task. For a two alternative discrimination task, the decision is based on the activity of two pools of sensory neurons, each of which supports one of the alternative decisions (${{\displaystyle x}}_1^{up}\dots {{\displaystyle x}}_n^{up}$ for upward motion vs ${{\displaystyle x}}_1^{down}\dots {{\displaystyle x}}_n^{down}$ for downward motion in the presented case). The decision is based on the decision-variable, which corresponds to the difference of the summed activity of each pool of sensory neurons. Right panel: Choice probabilities are computed for a simple pooling model (100 neurons/pool), in which the model decision on each fixed duration trial is determined by which pool has the largest summed activity . We vary both the extent of correlation between pairs of neurons belonging to the same pool (r_within), and the correlation for pairs where the neurons come from different pools (r_between), over the range of empirically observed values. The results are plotted as a function of the mean correlation, and the difference. Independent of the average value of noise correlation, the size of the observed choice probability depends upon difference in correlation (r_within – r_between) between these two pairings. The two black markers show how these values changed with task instruction in the study by Cohen and Newsome. To compute these numbers we assumed that all neurons whose preferred direction has a vector component to the right contribute to the rightward pool.