Existing efforts to understand the neural circuits for decision-making have primarily focused on excitatory neurons, and have generally considered them as a single group. For example, analyses of population activity during decision-making and other behaviours have mainly left unconsidered the cell class of each neuron included in the analysis. In both experiments and models, this omission is surprising because classic and emerging work has uncovered tremendous diversity among excitatory pyramidal neurons. Putative classes of excitatory neurons differ in terms of laminar position, projection target, gene expression pattern, and developmental lineage. To determine whether knowledge of pyramidal neuron cell class is relevant to understanding decision-related circuits, we investigated the activity of 3 excitatory cell classes during perceptual decision-making in experts, and also during the novice to expert transition. Two cell types were defined based on their developmental lineage (FezF2 and PlexinD1) and a third type was defined via projection type (corticostriatal projection neurons). We compared activity across mice with calcium indicators that were restricted to these cell types via either genetic or viral strategies. For comparison, we also measured activity in mice in which calcium indicators were expressed in all excitatory neurons. We found that each cell class exhibits a distinct cortex-wide correlation structure and that this difference in correlation structures also affects the local structure of functionally identified cortical areas (defined via LocaNMF). Finally, although all classes exhibited a change in correlations over learning, the direction and magnitude of this change depended strongly on cell class. These observations reveal excitatory cell types as a critical component to consider in both experiments and modelling, and call for more experiments in which cell type is identified.
Anne Churchland is a Professor in Neurobiology at the David Geffen School of Medicine at UCLA. Her training included undergraduate studies at Wellesley College, a PhD from UCSF, and postdoctoral training at the University of Washington. She began her independent research career at Cold Spring Harbor in 2010 and remained there until coming to UCLA in 2020. The focus of her laboratory is understanding how auditory and visual stimuli are processed by the brain and used to guide decision-making. She combines experimental work with analysis and theoretical modeling to understand the neural circuits that perform the necessary computations for these decisions. She is also co-founder and executive board member of the International Brain Laboratory, serves on the Advisory Committee to the Director of the National Institutes of Health, and was a member of the BRAIN2.0 Working Group for the BRAIN Initiative.
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