Before revision: A major challenge in sensory neuroscience is to understand how complex stimuli are collectively encoded by neural circuits. In particular, the role of correlations between output neuronsis still unclear. Here we introduce a general strategy to equip an arbitrary model of stimulus encoding by single neurons with a network of couplings between output neurons.
After revision: Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to equip an arbitrary model of stimulus encoding by single neurons with a network of couplings between output neurons.