The biological substrates of computation in the cortex remain elusive. While the remarkable recent progress of artificial neural systems has generated important insights into cortical function, these networks employ highly simplified linear model neurons. Real cortical neurons receive thousands of synaptic inputs distributed across extensive dendritic arbors that exhibit highly nonlinear properties, providing an opportunity for subcellular computation before final integration and output at the axon. Neuronal units endowed with nonlinear dendritic processing could provide their respective networks with increased power, flexibility, and/or efficiency. However, the contributions of dendrites to cortical computations underlying behavior remain unclear. Dendritic mechanisms may not be recruited during relevant in vivo circuit activity or may only serve to compensate for other biological constraints. In this talk, I will discuss my lab’s progress in evaluating the biophysical substrates, engagement, and utility of dendritic processing in the mammalian cortex. We apply imaging and electrophysiology in two model circuits to pursue complementary approaches to this problem. First, using the reciprocal circuitry between V1 and the retrosplenial cortex (RSC) in the mouse brain we are performing a multidisciplinary synapses-to-systems analysis of dendritic integration. Second, we are systematically analyzing the biophysical properties of single neurons in temporal cortex across the phylogenetic tree from humans to mice. We hope to use the results from these lines of inquiry to understand general principles of cortical design and function, and to facilitate the development of new artificial neural networks with enhanced capabilities.