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arXiv · 2105.05382

Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece

Luke Y. Prince·Roy Henha Eyono·Ellen Boven·Arna Ghosh·Joe Pemberton·Franz Scherr·Claudia Clopath·Rui Ponte Costa·Wolfgang Maass·Blake A. Richards·Cristina Savin·Katharina Anna Wilmes
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Abstract

We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks. The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues.

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Luke Y. Prince, Roy Henha Eyono, Ellen Boven, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Anna Wilmes. 2021-05-12. Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece. https://arxiv.org/abs/2105.05382

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