DDLC Seminars - Professor Francesco Bullo
Автор: DDLC seminar series
Загружено: 2026-02-12
Просмотров: 165
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Title: Biologically plausible computing: Navigating energy landscapes
Abstract: Deep learning models, despite their power, lag behind the biological brain in interpretability, energy efficiency, and physical plausibility. This presentation explores the mathematical design principles of biological neural circuits -- building upon the classic concept that neural activity is fundamentally driven by cost minimization and energy landscapes. We demonstrate a direct mathematical equivalence between the firing dynamics of recurrent neural networks and "proximal gradient descent," a novel dynamical system used to solve optimization problems. This framework provides a top-down explanation for how biological networks process information, illustrated through examples such as sparse signal reconstruction in the visual cortex and decision-making via the free energy principle. Finally, we extend these concepts to complex excitatory-inhibitory circuits, modeling neurons as players in a mathematical game. We conclude by briefly discussing how these biological insights can inspire next-generation analog and neuromorphic computing.
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