Pith. sign in

REVIEW 2 cited by

EngramNCA: a Neural Cellular Automaton Model of Memory Transfer

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2504.11855 v1 pith:FDWXOAHU submitted 2025-04-16 cs.NE cs.AI

classification cs.NEcs.AI
keywords memoryengramncamorphologiesprivatevisibleautomatonbiologicalcells
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

This study introduces EngramNCA, a neural cellular automaton (NCA) that integrates both publicly visible states and private, cell-internal memory channels, drawing inspiration from emerging biological evidence suggesting that memory storage extends beyond synaptic modifications to include intracellular mechanisms. The proposed model comprises two components: GeneCA, an NCA trained to develop distinct morphologies from seed cells containing immutable "gene" encodings, and GenePropCA, an auxiliary NCA that modulates the private "genetic" memory of cells without altering their visible states. This architecture enables the encoding and propagation of complex morphologies through the interaction of visible and private channels, facilitating the growth of diverse structures from a shared "genetic" substrate. EngramNCA supports the emergence of hierarchical and coexisting morphologies, offering insights into decentralized memory storage and transfer in artificial systems. These findings have potential implications for the development of adaptive, self-organizing systems and may contribute to the broader understanding of memory mechanisms in both biological and synthetic contexts.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Path to Universal Neural Cellular Automata

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A single neural cellular automaton rule, conditioned on a learnable hardware state, performs matrix multiplication, translation, rotation, and a block-decomposed MNIST classification.

  2. ARC-NCA: Towards Developmental Solutions to the Abstraction and Reasoning Corpus

    cs.AI 2025-05 conditional novelty 5.0 of 10

    ARC-NCA shows that per-task test-time training of Neural Cellular Automata can solve about 13 percent of a 262-task ARC-AGI subset, but the claimed parity with ChatGPT 4.5 relies on results from different benchmark splits.

Pith tools