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Abstract In social science, formal and quantitative models, ranging from ones that describe economic growth to collective action, are used to formulate mechanistic explanations of the observed phenomena, provide predictions, and uncover new research questions. Here, we demonstrate the use of a machine learning system to aid the discovery of symbolic models that capture non-linear and dynamical relationships in social science datasets. By extending neuro-symbolic methods to find compact functions and differential equations in noisy and longitudinal data, we show that our system can be used to discover interpretable models from real-world data in economics and sociology. Augmenting existing workflows with symbolic regression can help uncover novel relationships and explore counterfactual models during the scientific process. We propose that this AI-assisted framework can bridge parametric and non-parametric models commonly employed in social science research by systematically exploring the space of non-linear models and enabling fine-grained control over expressivity and interpretability. 

Abstract Neuronal cell death and subsequent brain dysfunction are hallmarks of aging and neurodegeneration, but how the nearby healthy neurons (bystanders) respond to the death of their neighbors is not fully understood. In theDrosophilalarval neuromuscular system, bystander motor neurons can structurally and functionally compensate for the loss of their neighbors by increasing their terminal bouton number and activity. We term this compensation as cross-neuron plasticity, and in this study, we demonstrate that theDrosophilaengulfment receptor, Draper, and the associated kinase, Shark, are required for cross-neuron plasticity. Overexpression of the Draper-I isoform boosts cross-neuron plasticity, implying that the strength of plasticity correlates with Draper signaling. In addition, we find that functional cross-neuron plasticity can be induced at different developmental stages. Our work uncovers a role for Draper signaling in cross-neuron plasticity and provides insights into how healthy bystander neurons respond to the loss of their neighboring neurons.