AbstractTo study sensory representations, neuroscientists record neural activities while presenting different stimuli to the animal. From these data, we identify neurons whose activities depend systematically on each aspect of the stimulus. These neurons are said to be “tuned” to that stimulus feature. It is typically assumed that these tuned neurons represent the stimulus feature in their firing, whereas any “untuned” neurons do not contribute to its representation. Recent experimental work questioned this assumption, showing that in some circumstances, neurons that are untuned to a particular stimulus feature can contribute to its representation. These findings suggest that, by ignoring untuned neurons, our understanding of population coding might be incomplete. At the same time, several key questions remain unanswered: Are the impacts of untuned neurons on population coding due to weak tuning that is nevertheless below the threshold the experimenters set for calling neurons tuned (vs untuned)? Do these effects hold for different population sizes and/or correlation structures? And could neural circuit function ever benefit from having some untuned neurons vs having all neurons be tuned to the stimulus? Using theoretical calculations and analyses of in vivo neural data, I answer those questions by: a) showing how, in the presence of correlated variability, untuned neurons can enhance sensory information coding, for a variety of population sizes and correlation structures; b) demonstrating that this effect does not rely on weak tuning; and c) identifying conditions under which the neural code can be made more informative by replacing some of the tuned neurons with untuned ones. These conditions specify when there is a functional benefit to having untuned neurons.Author SummaryIn the visual system, most neurons’ firing rates are tuned to various aspects of the stimulus (motion, contrast, etc.). For each stimulus feature, however some neurons appear to be untuned: their firing rates do not depend on that stimulus feature. Previous work on information coding in neural populations ignored untuned neurons, assuming that only the neurons tuned to a given stimulus feature contribute to its encoding. Recent experimental work questioned this assumption, showing that neurons with no apparent tuning can sometimes contribute to information coding. However, key questions remain unanswered. First, how do the untuned neurons contribute to information coding, and could this effect rely on those neurons having weak tuning that was overlooked? Second, does the function of a neural circuit ever benefit from having some neurons untuned? Or should every neuron be tuned (even weakly) to every stimulus feature? Here, I use mathematical calculations and analyses of data from the mouse visual cortex to answer those questions. First, I show how (and why) correlations between neurons enable the untuned neurons to contribute to information coding. Second, I show that neural populations can often do a better job of encoding a given stimulus feature when some of the neurons are untuned for that stimulus feature. Thus, it may be best for the brain to segregate its tuning, leaving some neurons untuned for each stimulus feature. Along with helping to explain how the brain processes external stimuli, this work has strong implications for attempts to decode brain signals, to control brain-machine interfaces: better performance could be obtained if the activities of all neurons are decoded, as opposed to only those with strong tuning.
The Geometry of Information Coding in Correlated Neural Populations
