Journal
CURRENT OPINION IN NEUROBIOLOGY
Volume 46, Issue -, Pages 1-6Publisher
CURRENT BIOLOGY LTD
DOI: 10.1016/j.conb.2017.06.003
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Funding
- Israel Science Foundation [346/16]
- European Research Council FP7 Career Integration Grant [2013-618543]
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Recurrent neural networks (RNNs) are a class of computational models that are often used as a tool to explain neurobiological phenomena, considering anatomical, electrophysiological and computational constraints. RNNs can either be designed to implement a certain dynamical principle, or they can be trained by input-output examples. Recently, there has been large progress in utilizing trained RNNs both for computational tasks, and as explanations of neural phenomena. I will review how combining trained RNNs with reverse engineering can provide an alternative framework for modeling in neuroscience, potentially serving as a powerful hypothesis generation tool. Despite the recent progress and potential benefits, there are many fundamental gaps towards a theory of these networks. I will discuss these challenges and possible methods to attack them.
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