Neural Learning Rules from Associative Networks Theory

• URL: http://arxiv.org/abs/2503.19922v1
• Date: Tue, 11 Mar 2025 11:44:04 GMT
• Title: Neural Learning Rules from Associative Networks Theory
• Authors: Daniele Lotito,
• Abstract summary: Associative networks theory is providing tools to interpret update rules of artificial neural networks.<n> deriving neural learning rules from a solid theory remains a fundamental challenge.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Associative networks theory is increasingly providing tools to interpret update rules of artificial neural networks. At the same time, deriving neural learning rules from a solid theory remains a fundamental challenge. We make some steps in this direction by considering general energy-based associative networks of continuous neurons and synapses that evolve in multiple time scales. We use the separation of these timescales to recover a limit in which the activation of the neurons, the energy of the system and the neural dynamics can all be recovered from a generating function. By allowing the generating function to depend on memories, we recover the conventional Hebbian modeling choice for the interaction strength between neurons. Finally, we propose and discuss a dynamics of memories that enables us to include learning in this framework.

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