Partial recurrence can enable robust and efficient computation
File(s)
Published version
Author(s)
Ghosh, Marcus
Goodman, Dan
Type
Journal Article
Abstract
Neural circuits are sparse and bidirectional. Meaning that signals flow from early sensory areas to later regions and back. Yet, between connected areas there exist some but not all pathways. How does this structure, somewhere between feedforward and fully recurrent, shape circuit function? To address this question, we designed a recurrent neural network model in which a set of weight matrices (i.e. pathways) can be combined to generate every network structure between feedforward and fully recurrent. We term these architectures partially recurrent neural networks (pRNNs). We trained over 25,000 pRNNs on a novel set of reinforcement learning tasks, designed to mimic multisensory navigation, and compared their performance across multiple functional metrics. Our findings reveal three key insights. First, in dense-cue environments, most pRNN architectures match or exceed the task performance, learning speed or robustness of fully recurrent networks, despite using as few as one quarter the number of parameters; in sparse-cue environments, many match but a substantial fraction underperform. These results demonstrate that partial recurrence can enable energy efficient, yet performant solutions. Second, each pathway’s functional impact is both task and circuit dependent. For instance, feedback connections enhance robustness to noise in some, but not all contexts. Third, different pRNN architectures learn solutions with distinct input sensitivities and memory dynamics, and these computational traits help to explain their functional capabilities. Overall, our results demonstrate that partial recurrence can enable robust and efficient computation- a finding that may help to explain why neural circuits are sparse and bidirectional, and shows how these principles can inform the design of artificial systems.
Date Issued
2026-08-07
Date Acceptance
2026-07-08
Citation
Communications AI and computing, 2026, 1
ISSN
3091-292X
Publisher
Nature Portfolio
Journal / Book Title
Communications AI and computing
Volume
1
Copyright Statement
© The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
11
Date Publish Online
2026-08-07