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A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

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Encoding innate ability through a genomic bottleneck.

Sergey Shuvaev¹, Divyansha Lachi¹, Alexei Koulakov¹

¹Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724.

Proceedings of the National Academy of Sciences of the United States of America

|September 12, 2024

Summary

This summary is machine-generated.

The genome compresses neural circuit information, similar to lossy compression in artificial neural networks. This "genomic bottleneck" allows innate behaviors and enhances AI learning through regularization and transfer learning.

Keywords:

AI machine learning neural computation neural networks

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Area of Science:

Neuroscience
Artificial Intelligence
Evolutionary Biology

Background:

Animals possess innate behaviors stemming from genetically encoded neural circuits.
The genome's information capacity is insufficient to specify complex neural connectivity, implying a "genomic bottleneck" for inherited circuit rules.

Purpose of the Study:

To model innate behavioral capacity using artificial neural networks and lossy compression.
To investigate the role of the genomic bottleneck in neural circuit formation and adaptation.

Main Methods:

Formulating innate behavioral capacity as lossy compression of artificial neural network weight matrices.
Analyzing compression of standard network architectures and their pretraining performance.

Main Results:

Several network architectures achieved significant compression (orders of magnitude) with pretraining performance near fully trained networks.
The genomic bottleneck algorithm demonstrated enhanced transfer learning for complex tasks, capturing essential circuit features.

Conclusions:

Neural circuit compression via the genomic bottleneck acts as a regularizer, facilitating evolution of adaptable circuits.
The genomic bottleneck offers insights into innate priors for AI, complementing traditional learning approaches.