Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization

• URL: http://arxiv.org/abs/2604.05042v1
• Date: Mon, 06 Apr 2026 18:00:17 GMT
• Title: Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
• Abstract summary: Recent advances in machine learning have revealed novel mechanisms by which dynamical systems perform computation.<n>This tutorial focuses on neuro-inspired approaches to computation that aim to improve scalability, robustness, and energy efficiency.
• Score: 6.977686048058014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advances encompass a wide range of conceptual, mathematical, and computational ideas, with applications for model learning and training, memory retrieval, data-driven control, and optimization. This tutorial focuses on neuro-inspired approaches to computation that aim to improve scalability, robustness, and energy efficiency across such tasks, bridging the gap between artificial and biological systems. Particular emphasis is placed on energy-based dynamical models that encode information through gradient flows and energy landscapes. We begin by reviewing classical formulations, such as continuous-time Hopfield networks and Boltzmann machines, and then extend the framework to modern developments. These include dense associative memory models for high-capacity storage, oscillator-based networks for large-scale optimization, and proximal-descent dynamics for composite and constrained reconstruction. The tutorial demonstrates how control-theoretic principles can guide the design of next-generation neurocomputing systems, steering the discussion beyond conventional feedforward and backpropagation-based approaches to artificial intelligence.

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