Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory "VaCoAl" as a Substrate for Vector-HaSH and TEM
The paper discusses a new memory architecture called VaCoAl, which integrates concepts from computational neuroscience and hyperdimensional engineering. It proposes that VaCoAl can serve as a substrate for existing models of memory, specifically Vector-HaSH and the Tolman-Eichenbaum Machine. The authors provide formal correspondences and predictions that bridge these fields, emphasizing the architectural similarities with evolutionary conserved pathways in the brain.
- ▪VaCoAl is an algebro-deterministic hyperdimensional memory architecture built from Galois-field LFSRs.
- ▪The architecture provides a substrate-level alternative to existing memory models, satisfying quasi-orthogonality requirements.
- ▪The study derives testable predictions based on the proposed model and its correspondence with hippocampal computation.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15652 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
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| Summary source text | contentText |
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| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Neural and Evolutionary Computing arXiv:2605.15652 (cs) [Submitted on 15 May 2026] Title:Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory "VaCoAl" as a Substrate for Vector-HaSH and TEM Authors:Hiroyuki Chuma, Kanji Otsuka, Yoichi Sato View a PDF of the paper titled Bridging Silicon and the Hippocampus: Algebro-Deterministic Memory "VaCoAl" as a Substrate for Vector-HaSH and TEM, by Hiroyuki Chuma and 2 other authors View PDF HTML (experimental) Abstract:Vector-HaSH and the Tolman-Eichenbaum Machine (TEM) propose that the hippocampal-entorhinal circuit factorizes content from a prestructured grid-cell scaffold and supports compositional memory via ripple-mediated replay.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.