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Deep learning in computational neuroscience

Abstract

Deep learning has revolutionized the information industry and already pervades many aspects of our daily lives. Slowly, scientific research is also being transformed by deep learning because it allows researchers to model complex, highly nonlinear relationships and distributions from data even when there is no analytical model available. The possible applications of deep learning in neuroscience are countless: it can be used as a data processing tool for complex neurophysiological, anatomical, and behavioral data, for predictive modeling and system identification, as a model for complex naturalistic stimuli, or as a model for representations in sensory systems. However, researchers also have to be aware of a number of pitfalls when using deep learning as a tool for science, such as limited ability to generalize across data domains, adversarial examples, and inherent biases learned from the training datasets. Furthermore, a key question that is not fully answered is how to open the black box to distill knowledge about the biological system.

With this workshop, we want to continue the tradition of the last two years and raise awareness for the tremendous opportunities of deep learning in (computational) neuroscience by showcasing a diverse set successful applications, as well as the limits and active areas of research at present.

 

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