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Encoding innate ability through a genomic bottleneck. | LitMetric

Encoding innate ability through a genomic bottleneck.

Proc Natl Acad Sci U S A

Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724.

Published: September 2024

AI Article Synopsis

Article 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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11420173PMC
http://dx.doi.org/10.1073/pnas.2409160121DOI Listing

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