Learning and attention reveal a general relationship between population activity and behavior

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Science  26 Jan 2018:
Vol. 359, Issue 6374, pp. 463-465
DOI: 10.1126/science.aao0284

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The neuronal population is the key unit

The responses of pairs of neurons to repeated presentations of the same stimulus are typically correlated, and an identical neuronal population can perform many functions. This suggests that the relevant units of computation are not single neurons but subspaces of the complete population activity. To test this idea, Ni et al. measured the relationship between neuronal population activity and performance in monkeys. They investigated attention, which improves perception of attended stimuli, and perceptual learning, which improves perception of well-practiced stimuli. These two processes operate on different time scales and are usually studied using different perceptual tasks. Manipulation of attention and learning in the same behavioral trials and the same neuronal populations revealed the dimensions of population activity that matter most for behavior.

Science, this issue p. 463


Prior studies have demonstrated that correlated variability changes with cognitive processes that improve perceptual performance. We tested whether correlated variability covaries with subjects’ performance—whether performance improves quickly with attention or slowly with perceptual learning. We found a single, consistent relationship between correlated variability and behavioral performance, regardless of the time frame of correlated variability change. This correlated variability was oriented along the dimensions in population space used by the animal on a trial-by-trial basis to make decisions. That subjects’ choices were predicted by specific dimensions that were aligned with the correlated variability axis clarifies long-standing paradoxes about the relationship between shared variability and behavior.

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