Feedback control guides credit assignment in recurrent neural networks
File(s)Kaleb.pdf (2.86 MB)
Accepted version
Author(s)
Kaleb, K
Feulner, B
Gallego, JA
Clopath, C
Type
Conference Paper
Abstract
How do brain circuits learn to generate behaviour? While significant strides have been made in understanding learning in artificial neural networks, applying this knowledge to biological networks remains challenging. For instance, while backpropagation is known to perform accurate credit assignment of error in artificial neural networks, how a similarly powerful process can be realized within the constraints of biological circuits remains largely unclear. One of the major challenges is that the brain's extensive recurrent connectivity requires the propagation of error through both space and time, a problem that is notoriously difficult to solve in vanilla recurrent neural networks. Moreover, the extensive feedback connections in the brain are known to influence forward network activity, but the interaction between feedback-driven activity changes and local, synaptic plasticity-based learning is not fully understood. Building on our previous work modelling motor learning, this work investigates the mechanistic properties of pre-trained networks with feedback control on a standard motor task. We show that feedback control of the ongoing recurrent network dynamics approximates the optimal first-order gradient with respect to the network activities, allowing for rapid, ongoing movement correction. Moreover, we show that trial-by-trial adaptation to a persistent perturbation using a local, biologically plausible learning rule that integrates recent activity and error feedback is both more accurate and more efficient with feedback control during learning, due to the decoupling of the recurrent network dynamics and the injection of an adaptive, second-order gradient into the network dynamics. Thus, our results suggest that feedback control may guide credit assignment in biological recurrent neural networks, enabling both rapid and efficient learning in the brain.
Date Issued
2025-02-01
Date Acceptance
2024-12-01
Citation
Advances in Neural Information Processing Systems, 2025, 37, pp.5122-5144
ISBN
9798331314385
ISSN
1049-5258
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Start Page
5122
End Page
5144
Journal / Book Title
Advances in Neural Information Processing Systems
Volume
37
Copyright Statement
© 2024 Neural Information Processing Systems Foundation, Inc. (NeurIPS).
Source
NeurIPS 2024
Publication Status
Published
Start Date
2024-12-10
Finish Date
2024-12-15
Coverage Spatial
Vancouver, Canada