Project Summary A key goal in neuroscience is determining how microcircuit structure predicts circuit function. An intriguing idea, supported by some theoretical models, is that variation in microcircuit composition supports functional specialization. This theory has received support from the observation of a correlation between gradients in circuit properties (receptor expression densities; inhibitory cell types) and in measurements of average intrinsic timescales of recorded activity across cortical areas. However, other theories show how observed hierarchies of timescales can emerge in the absence of microcircuit variation, either in critically tuned random networks or in feed-forward networks with each layer summing over correlated sets of inputs. Moreover, individual cells are embedded in complex, interconnected networks, generating correlations on multiple timescales and broad distributions of single-cell timescales even within a single region. Previous empirical investigations have relied on quantifying timescales of activity using single-cell spike count correlations and by averaged readouts of activity such as the electrocorticogram (ECoG), because until recently, massively parallel recordings of populations of single neurons with cell-type information as well as the statistical methods to analyze collective dynamics were not widely available. We have developed an analytic framework based on dynamic latent variable models to quantify timescales and states of cortical dynamics from spiking activity in local populations and from local measures of population activity (the local field potential, LFP). We propose to apply these methods to a set of publicly accessible recordings of cortical activity in the mouse to determine (1) the extent to which variation in specific features of the cortical microcircuitry explains variation in cortical dynamics, (2) the role of specific cell types in determining collective dynamics, and (3) the connection between activity models across recording modalities This study is fundamentally about quantitatively mapping normal variation in function and determining the extent to which that variation is predicted from the local circuit properties. Modeling this relationship accurately will have a large impact on our ability to predict how neural dynamics arise from changes in microcircuit structure and could be extended to understand disruption of activity dynamics arising from circuit changes linked to mental health disorders. Independent of the relationship to microcircuit structure, success of this study will generate a framework in which variability of cortical dynamics can be accurately and quantitatively mapped across individuals or in the same individual over time, providing an invaluable tool for the studies of learning and development.