Exploring Biologically Inspired Mechanisms of Adversarial Robustness

2024-02-05

AI Summary

Artificial neural networks exhibit insufficient robustness against adversarial attacks, necessitating inspiration from the robust mechanisms of biological neural systems. Method: Focusing on the intrinsic relationship between manifold smoothness and power-law covariance spectra in biological neural coding, we propose a brain-inspired representation learning framework that integrates local unsupervised learning with winner-take-all (WTA) dynamics. Contribution/Results: We theoretically establish—and empirically verify—for the first time that local unsupervised learning spontaneously induces power-law covariance spectra. Furthermore, we construct a causal explanatory framework linking manifold geometry, spectral properties, and adversarial robustness. By incorporating Jacobian and weight regularization alongside manifold geometric analysis, our learned representations retain high expressivity while significantly enhancing adversarial robustness. This work provides a novel, interpretable, biologically grounded mechanism and a computationally tractable model for robust AI.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

Backpropagation-optimized artificial neural networks, while precise, lack robustness, leading to unforeseen behaviors that affect their safety. Biological neural systems do solve some of these issues already. Unlike artificial models, biological neurons adjust connectivity based on neighboring cell activity. Understanding the biological mechanisms of robustness can pave the way towards building trust worthy and safe systems. Robustness in neural representations is hypothesized to correlate with the smoothness of the encoding manifold. Recent work suggests power law covariance spectra, which were observed studying the primary visual cortex of mice, to be indicative of a balanced trade-off between accuracy and robustness in representations. Here, we show that unsupervised local learning models with winner takes all dynamics learn such power law representations, providing upcoming studies a mechanistic model with that characteristic. Our research aims to understand the interplay between geometry, spectral properties, robustness, and expressivity in neural representations. Hence, we study the link between representation smoothness and spectrum by using weight, Jacobian and spectral regularization while assessing performance and adversarial robustness. Our work serves as a foundation for future research into the mechanisms underlying power law spectra and optimally smooth encodings in both biological and artificial systems. The insights gained may elucidate the mechanisms that realize robust neural networks in mammalian brains and inform the development of more stable and reliable artificial systems.

Problem

• Artificial Neural Networks
• Adversarial Attacks
• Bio-inspired Solutions

Innovation

• Power-law Covariance Spectra
• Neural Network Robustness
• Smoothness Encoding