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

L. Y. Prince, R. H. Eyono, E. Boven, A. Ghosh, J. Pemberton, F. Scherr, C. Clopath, R. P. Costa, W. Maass, B. A. Richards, C. Savin, and K. A. Wilmes

Abstract:

This perspective piece came about through the Generative Adversarial Collaboration (GAC) series of workshops organized by the Computational Cognitive Neuroscience (CCN) conference in 2020. We brought together a number of experts from the field of theoretical neuroscience to debate emerging issues in our understanding of how learning is implemented in biological recurrent neural networks. Here, we will give a brief review of the common assumptions about biological learning and the corresponding findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks commonly used in artificial intelligence. We will then outline the key issues discussed in the workshop: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions. Finally, we conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help to bring clarity to these issues.

Reference: L. Y. Prince, R. H. Eyono, E. Boven, A. Ghosh, J. Pemberton, F. Scherr, C. Clopath, R. P. Costa, W. Maass, B. A. Richards, C. Savin, and K. A. Wilmes. Current state and future directions for learning in biological recurrent neural networks: A perspective piece. Neurons, Behavior, Data analysis, and Theory, 1, 2022.