Search

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions. Behavioural relevance: If a single-trial response is more similar to the average template, it is more likely to evoke correct behavioural responses. We apply this test to two data sets: (1) Two-photon recordings in primary somatosensory cortices (S1 and S2) of mice trained to detect optogenetic stimulation in S1; and (2) Electrophysiological recordings from 71 brain areas in mice performing a contrast discrimination task. Under the highly controlled settings of Data set 1, both assumptions were largely fulfilled. In contrast, the less restrictive paradigm of Data set 2 met neither assumption. Simulations predict that the larger diversity of neuronal response preferences, rather than higher cross-trial reliability, drives the better performance of Data set 1. We conclude that when behaviour is less tightly restricted, average responses do not seem particularly relevant to neuronal computation, potentially because information is encoded more dynamically. Most importantly, we encourage researchers to apply this simple test of computational relevance whenever using trial-averaged neuronal metrics, in order to gauge how representative cross-trial averages are in a given context. Author summary: Neuronal activity is highly dynamic—our brain never responds to the same situation in exactly the same way. How do we extract information from such dynamic signals? The classical answer is: averaging neuronal activity across repetitions of the same stimulus to detect its consistent aspects. This logic is widespread—it is hard to find a neuroscience study that does not contain averages. But how well do averages represent the computations that happen in the brain moment by moment? We developed a simple test that probes two assumptions implicit in averaging: Reliability: Neuronal responses repeat consistently enough across stimulus repetitions that the average remains recognizable. Behavioural relevance: Neuronal responses that are more similar to the average, are more likely to evoke correct behaviour. We apply this test to two example data sets featuring population recordings in mice performing perceptual tasks. We show that both assumptions were largely fulfilled in the first data set, but not in the second; suggesting that the relevance of averaging varies across contexts, e.g. due to experimental control levels and neuronal diversity. Most importantly, we encourage neuroscientists to use our test to gauge whether averages reflect informative aspects of neuronal activity in their data. [ABSTRACT FROM AUTHOR]