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Abstract Normative models of synaptic plasticity use computational rationales to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work in this realm, but experimental confirmation remains limited. In this review, we organize work on normative plasticity models in terms of a set of desiderata that, when satisfied, are designed to ensure that a given model demonstrates a clear link between plasticity and adaptive behavior, is consistent with known biological evidence about neural plasticity and yields specific testable predictions. As a prototype, we include a detailed analysis of the REINFORCE algorithm. We also discuss how new models have begun to improve on the identified criteria and suggest avenues for further development. Overall, we provide a conceptual guide to help develop neural learning theories that are precise, powerful, and experimentally testable. 

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments. Significance StatementNeuroscientists heavily rely on artificial perturbation of the neural activity to understand the function of brain circuits. Current interpretations of experimental results often follow a simple logic, that the magnitude of a behavioral effect following a perturbation indicates the degree of involvement of the perturbed circuit in the behavior. We model a variety of neural networks with controlled levels of com­plexity, robustness, and plasticity, showing that perturbation experiments could yield counter-intuitive results when networks are complex enough-to allow unperturbed pathways to compensate for the per­turbed neurons-or plastic enough-to allow continued learning from feedback during perturbations. To rein in these complexities we develop a Functional Integrity Index that captures alterations in network computations and predicts disruptions of behavior with the perturbation.