ArticleNature communications2025
Dynamics of specialization in neural modules under resource constraints.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Inferring brain-wide interactions using data-constrained recurrent neural network models.Neuron · 2026Article
- Compositionality and systematicity emerge from iterated learning in deep linear networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.Frontiers in computational neuroscience · 2026Article
- Mutual information minimization enhances noise robustness and induces fault containment in functionally differentiated recurrent neural networks.Frontiers in neuroscience · 2026Article
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Authors and funding
2 authors.
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Abstract
The brain is structurally and functionally modular, although recent evidence has raised questions about the extent of both types of modularity. Using a simple, toy artificial neural network setup that allows for precise control, we find that structural modularity does not in general guarantee functional specialization (across multiple measures of specialization). Further, in this setup (1) specialization only emerges when features of the environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across several different variations of network architectures. Finally, we show that functional specialization varies dynamically across time, and these dynamics depend on both the timing and bandwidth of information flow in the network. We conclude that a static notion of specialization is likely too simple a framework for understanding intelligence in situations of real-world complexity, from biology to brain-inspired neuromorphic systems.
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Registered trials
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