Precise Extraction of Neural Motifs Reveals Multiscale, Parallel Encoding Schemes in Auditory Cortex.

Neural population activity is often stereotyped into recurring activity patterns, i.e., neural motifs, which can be seen as the fundamental building blocks in sensory processing and cognition. In this work we study the codes carried by such neural motifs in primary auditory cortex A1, and analyze how they build on and complement traditional views of single-unit coding. In particular, we study, using two-photon calcium imaging (CI), how activity in A1 differentially represents both sensory stimuli and task and behavioral variables in each of the parallel single neuron and population motif scales. While CI enables the study of neural motifs by capturing the activity of large neural populations, identifying motifs in CI is hampered by the temporal imprecision of current motif-detection algorithms when applied to CI data. We thus developed a new algorithm for motif detection, which enabled us to identify widespread stimulus-encoding motifs as well as a small number of motifs jointly encoding stimulus and choice in Layer 2/3 of A1. These motifs consist of small groups of neurons that are neither clustered nor regularly ordered in space. Interestingly, active neurons within task-encoding motifs exhibit mixed encoding properties inconsistent with the motifs they participate in. Together, these results demonstrate how single unit activity and neural motifs in A1 L2/3 provide different levels of coding granularity containing different information in parallel within the greater neural population. Generally, our results indicate that downstream populations, by selecting which scale of a population drives them, can be selective in the information collected for later cognitive processing.