Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
What Do Hebbian Learners Learn? Reduction Axioms for Iterated Hebbian Learning
Authors: Caleb Schultz Kisby, SaΓΊl A. Blanco, Lawrence S. Moss
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper is a contribution to neural network semantics, a foundational framework for neuro-symbolic AI. The key insight of this theory is that logical operators can be mapped to operators on neural network states. In this paper, we do this for a neural network learning operator. We map a dynamic operator [Ο] to iterated Hebbian learning, a simple learning policy that updates a neural network by repeatedly applying Hebb s learning rule until the net reaches a fixed-point. Our main result is that we can translate away [Ο]-formulas via reduction axioms. This means that completeness for the logic of iterated Hebbian learning follows from completeness of the base logic. These reduction axioms also provide (1) a humaninterpretable description of iterated Hebbian learning as a kind of plausibility upgrade, and (2) an approach to building neural networks with guarantees on what they can learn. |
| Researcher Affiliation | Academia | Caleb Schultz Kisby1, Sa ul A. Blanco1, Lawrence S. Moss2 1Department of Computer Science, Indiana University 2Department of Mathematics, Indiana University Bloomington, IN 47408, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The proofs of our main theorem and its major supporting lemmas have been verified using the Lean 4 interactive theorem prover (Moura and Ullrich 2021). The code and installation instructions are available at https://github.com/ais-climber/AAAI2024 |
| Open Datasets | No | This is a theoretical paper focused on logical formalisms and proofs, not empirical experiments. Therefore, it does not involve training models on datasets. |
| Dataset Splits | No | This is a theoretical paper focused on logical formalisms and proofs, not empirical experiments involving dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and focuses on logical proofs and formal systems. It does not describe any empirical experiments or the specific hardware used for computations or proof verification. |
| Software Dependencies | Yes | The proofs of our main theorem and its major supporting lemmas have been verified using the Lean 4 interactive theorem prover (Moura and Ullrich 2021). |
| Experiment Setup | No | The paper is theoretical and focuses on logical formalisms and proofs. It does not describe an experimental setup with hyperparameters, training configurations, or system-level settings. |