Figure 6 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Laminar profile of attention effects. A) Example session CSD profile evoked by task stimulus (left column) with multi-unit receptive fields (middle) and tuning curves (right). Depths are relative to first L5 channel. Dotted black line shows L4-5 transition. Arrow shows initial current sinksource flip in L4C. B) Fractional increase in firing rates in AB and AI, relative to AO, conditions split by laminar group. C) Spike count correlation over 0-1s interval split by laminar group. Data in B-C show mean across sessions ± SEM (N=27).
Source publication
Variability in neuronal responses to identical stimuli is frequently correlated across a population. Attention is thought to reduce these correlations by suppressing noisy inputs shared by the population. However, even with precise control of the visual stimulus, the subject’s attentional state varies across trials. While these state fluctuations a...
Citations
Spike-time correlations capture the short timescale covariance between the activity of neurons on a single trial. These correlations can significantly vary in magnitude and sign from trial to trial, and have been proposed to contribute to information encoding in visual cortex. While monkeys performed a motion-pulse detection task, we examined the behavioral impact of both the magnitude and sign of single-trial spike-time correlations between two nonoverlapping pools of middle temporal (MT) neurons. We applied three single-trial measures of spike-time correlation between our multiunit MT spike trains (Pearson’s, absolute value of Pearson’s, and mutual information), and examined the degree to which they predicted a subject’s performance on a trial-by-trial basis. We found that on each trial, positive and negative spike-time correlations were almost equally likely, and, once the correlational sign was accounted for, all three measures were similarly predictive of behavior. Importantly, just before the behaviorally relevant motion pulse occurred, single-trial spike-time correlations were as predictive of the performance of the animal as single-trial firing rates. While firing rates were positively associated with behavioral outcomes, the presence of either strong positive or negative correlations had a detrimental effect on behavior. These correlations occurred on short timescales, and the strongest positive and negative correlations modulated behavioral performance by ~9%, compared with trials with no correlations. We suggest a model where spike-time correlations are associated with a common noise source for the two MT pools, which in turn decreases the signal-to-noise ratio of the integrated signals that drive motion detection.
