Biological learning in key-value memory networks

• URL: http://arxiv.org/abs/2110.13976v1
• Date: Tue, 26 Oct 2021 19:26:53 GMT
• Title: Biological learning in key-value memory networks
• Authors: Danil Tyulmankov, Ching Fang, Annapurna Vadaparty, Guangyu Robert Yang
• Abstract summary: Memory-augmented neural networks in machine learning commonly use a key-value mechanism to store and read out memories in a single step. We propose an implementation of basic key-value memory that stores inputs using a combination of biologically plausible three-factor plasticity rules. Our results suggest a compelling alternative to the classical Hopfield network as a model of biological long-term memory.
• Score: 0.45880283710344055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In neuroscience, classical Hopfield networks are the standard biologically plausible model of long-term memory, relying on Hebbian plasticity for storage and attractor dynamics for recall. In contrast, memory-augmented neural networks in machine learning commonly use a key-value mechanism to store and read out memories in a single step. Such augmented networks achieve impressive feats of memory compared to traditional variants, yet their biological relevance is unclear. We propose an implementation of basic key-value memory that stores inputs using a combination of biologically plausible three-factor plasticity rules. The same rules are recovered when network parameters are meta-learned. Our network performs on par with classical Hopfield networks on autoassociative memory tasks and can be naturally extended to continual recall, heteroassociative memory, and sequence learning. Our results suggest a compelling alternative to the classical Hopfield network as a model of biological long-term memory.

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