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Coding Schemes In Neural Networks Learning Classification Tasks.

Home
Coding Schemes In Neural Networks Learning Classification Tasks.

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Coding schemes in neural networks learning classification tasks.

Alexander van Meegen¹, Haim Sompolinsky^2,3

¹Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA. alexander.vanmeegen@epfl.ch.

Nature Communications

|April 9, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Neural networks learn task-specific features, but their representations depend on neuronal nonlinearity. Linear networks use analog coding, while nonlinear networks exhibit sparse or redundant coding due to symmetry breaking.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Neural networks excel at learning task-dependent features.
The precise nature of these emergent representations remains poorly understood.
Understanding feature learning is key to advancing AI and neuroscience.

Purpose of the Study:

To investigate how learning shapes representations in wide, fully-connected neural networks.
To analyze the impact of neuronal nonlinearity on emergent feature representations.
To explore the Bayesian framework for understanding neural network weight posteriors.

Main Methods:

Utilized a Bayesian framework to model the posterior distribution of neural network weights.
Analyzed fully-connected, wide neural networks trained on classification tasks.
Focused on the feature learning ('non-lazy') regime of network operation.

Main Results:

Networks acquire strong, data-dependent features (coding schemes) where responses correlate with class membership.
The type of coding scheme critically depends on the neuronal nonlinearity.
Linear networks develop analog coding schemes; nonlinear networks exhibit sparse or redundant coding via symmetry breaking.

Conclusions:

Neuronal nonlinearity is a crucial factor determining the nature of emergent representations in neural networks.
Network properties like weight scaling and nonlinearity significantly shape learned representations.
Findings offer insights into the mechanisms of feature learning in artificial and biological neural systems.