Learning from the brain: leveraging biology for efficient and robust machine intelligence

Jocelyn Rego

doi:10.17918/00010818

Recent advancements in artificial intelligence have yielded impressive performance across domains; however, the deep learning methods that underlie these systems suffer from challenges in energy efficiency and remain vulnerable to exploits that degrade performance. As machine learning models grow in both size and popularity, improvements in energy efficiency and robustness are vital. In contrast, the brain has evolved to inherently express these capabilities. The organizational and functional principles that govern biological neural processing result in complex interactions that produce advanced, adaptable intelligence within physical constraints. In this work, we explore how key features of the brain can be leveraged to address critical challenges in machine learning, bridging the gap between biological and machine intelligence. By mimicking features of sparsity, competition, feedback, spiking communication, and local learning we develop inherently robust and efficient machine learning systems. We begin by exploring how biologically inspired sparse coding methods can detect and mitigate adversarial attacks that can mislead standard deep learning models. We develop hierarchical sparse coding models of visual processing in the brain at various levels and explore how features of lateral competition and top-down feedback create naturally robust and generalizable vision systems. We further demonstrate how the biologically plausible Locally Competitive Algorithm (LCA) for sparse coding can be applied to detect images corrupted by adversarial attacks. We explore novel spiking neuron models to balance computational efficiency and biological plausibility in frameworks compatible with energy efficient neuromorphic hardware. We provide evidence for sparse coding and spiking frameworks as valid models of processing in the brain, modeling neural responses with LCA and spiking neural networks (SNNs). Last, we present a novel energy-based architecture that implements LCA and Hopfield dynamics, combining sparse coding with energy-based models and equilibrium propagation for robust image classification and dictionary learning. Taken in sum, this work advances our understanding of biological and machine intelligence, laying the groundwork for the development of efficient and reliable artificial systems more aligned with the capabilities of the brain.

Learning from the brain: leveraging biology for efficient and robust machine intelligence

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Learning from the brain: leveraging biology for efficient and robust machine intelligence

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