Inverted Encoding Models Assay Population-Level Stimulus Representations, Not Single-Unit Neural Tuning

Inverted encoding models (IEMs) are a powerful tool for reconstructing population-level stimulus representations from aggregate measurements of neural activity (e.g., fMRI or EEG). In a recent report, Liu et al. (2018) tested whether IEMs can provide information about the underlying tuning of single units. Here, we argue that using stimulus reconstructions to infer properties of single neurons, such as neural tuning bandwidth, is an ill-posed problem with no unambiguous solution. Instead of interpreting results from these methods as evidence about single-unit tuning, we emphasize the utility of these methods for assaying population-level stimulus representations. These can be compared across task conditions to better constrain theories of large-scale neural information processing across experimental manipulations, such as changing sensory input or attention. Neuroscience methods range astronomically in scale. In some experiments, we record subthreshold membrane potentials in individual neurons, while in others we measure aggregate responses of thousands of neurons at the millimeter scale. A central goal in neuroscience is to bridge insights across all scales to understand the core computations underlying cognition (Churchland and Sejnowski, 1988). However, inferential problems arise when moving across scales: single-unit response properties cannot be inferred from fMRI activation in single voxels, subthreshold membrane potential cannot be inferred from extracellular spike rate, and the state of single ion channels cannot be inferred from intracellular recordings. These are all examples of an inverse problem in which an observation at a larger scale is consistent with an enormous number of possible observations at a smaller scale. Recent analytical advances have circumvented challenges inherent in inverse problems by instead transforming aggregate signals from their native “measurement space” (e.g., activation pattern across fMRI voxels) into a …