ArticleProceedings of the National Academy of Sciences of the United States of America2024
Encoding innate ability through a genomic bottleneck.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Neural rhythms as priors of speech computations.Patterns (New York, N.Y.) · 2026Article
- Dual computational systems in the development and evolution of mammalian brains.Science advances · 2026Article
- Article
- Leveraging insights from neuroscience to build adaptive artificial intelligence.Nature neuroscience · 2026Review
- Building the connectome of a small brain with a simple stochastic developmental generative model.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- Compression-based inference of network motif sets.PLoS computational biology · 2024Article
- Emergence of input selective recurrent dynamics via information transfer maximization.Scientific reports · 2024Article
- Variability in Neural Circuit Formation.Cold Spring Harbor perspectives in biology · 2024Review
- Functional neuronal circuits emerge in the absence of developmental activity.Nature communications · 2024Article
- Neurodynamical Computing at the Information Boundaries of Intelligent Systems.Cognitive computation · 2024Article
- Neuroscience Needs Network Science.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Neuroscience needs Network Science.ArXiv · 2023Article
- Complex computation from developmental priors.Nature communications · 2023Article
- Catalyzing next-generation Artificial Intelligence through NeuroAI.Nature communications · 2023Review
- Life is in motion (through a chick's eye).Animal cognition · 2023Review
- The impact of neuron morphology on cortical network architecture.Cell reports · 2022Article
- Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome. However, the information capacity of the genome is orders of magnitude smaller than that needed to specify the connectivity of an arbitrary brain circuit, indicating that the rules encoding circuit formation must fit through a "genomic bottleneck" as they pass from one generation to the next. Here, we formulate the problem of innate behavioral capacity in the context of artificial neural networks in terms of lossy compression of the weight matrix. We find that several standard network architectures can be compressed by several orders of magnitude, yielding pretraining performance that can approach that of the fully trained network. Interestingly, for complex but not for simple test problems, the genomic bottleneck algorithm also captures essential features of the circuit, leading to enhanced transfer learning to novel tasks and datasets. Our results suggest that compressing a neural circuit through the genomic bottleneck serves as a regularizer, enabling evolution to select simple circuits that can be readily adapted to important real-world tasks. The genomic bottleneck also suggests how innate priors can complement conventional approaches to learning in designing algorithms for AI.
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Registered trials
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