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
Signed reviews
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.
Forward citations
Cited by 2 Pith papers
-
A Path to Universal Neural Cellular Automata
A single neural cellular automaton rule, conditioned on a learnable hardware state, performs matrix multiplication, translation, rotation, and a block-decomposed MNIST classification.
-
ARC-NCA: Towards Developmental Solutions to the Abstraction and Reasoning Corpus
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.
Discussion (0). Continue with ORCID to comment.