Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 12 May 2021.
Builds on
8 of 43 references.
References · 8
- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2013Auto-Encoding Variational Bayescited by 4,053
- 2020Exploring Simple Siamese Representation Learningcited by 2,866
- 2018Experience Replay for Continual Learningcited by 1,095
- 2016Decoupled Neural Interfaces using Synthetic Gradientscited by 137
- 2019A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networkscited by 38
- 2018Continual Reinforcement Learning with Complex Synapsescited by 32
- 2017Cortical microcircuits as gated-recurrent neural networkscited by 6
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.