Interpretable modelling of input-output computations in cortex

Neurons receive input from thousands of synapses, which they transform into action potentials (APs) via their complex dendrites. How the dendritic location of these inputs, their timing, strength, and presynaptic origin impact AP output remains generally unknown. Here we demonstrate how to reveal which properties of the input causally underlie AP output, and how this input-output computation is influenced by the morphology and biophysical properties of the dendrites. For this purpose, we derive analytically tractable, interpretable models of the input-output computation that layer 5 pyramidal tract neurons (L5PTs) - the major output cell type of the cerebral cortex - perform upon sensory stimulation. We find that this input-output computation is preserved across L5PTs despite morphological and biophysical diversity. We show that three features are sufficient to explain in vivo observed sensory responses and receptive fields of L5PTs with high accuracy: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on this analytically tractable and interpretable description of the input-output computation, we show how to dissect the contributions of different input populations in thalamus and cortex to sensory responses of L5PTs. Thus, our approach provides a roadmap for revealing cellular input-output computations across different in vivo conditions. Author SummaryRevealing how synaptic inputs drive action potential output is one of the major challenges in neuroscience research. An increasing number of approaches therefore seek to combine detailed measurements at synaptic, cellular and network scales into biologically realistic brain models. Indeed, such models have started to make empirically testable predictions about the inputs that underlie in vivo observed activity patterns. However, the enormous complexity of these models generally prevents the derivation of interpretable descriptions that explain how neurons transform synaptic input into action potential output, and how these input-output computations depend on synaptic, cellular and network properties. Here we introduce an approach to reveal input-output computations that neurons in the cerebral cortex perform upon sensory stimulation. We reduce a realistic multi-scale cortex model to the minimal description that accounts for in vivo observed responses. Thereby, we identify the input-output computation that these cortical neurons perform under this in vivo condition, and we show that this computation is preserved across neurons despite morphological and biophysical diversity. Our approach provides analytically tractable and interpretable descriptions of neuronal input-output computations during specific in vivo conditions.