When cells think: a neuro-symbolic view of epigenetic regulation

Traditionally viewed as a set of switches regulating gene expression, epigenetic mechanisms may also operate as an information-processing system with symbolic and subsymbolic features. In this framework, gene-specific DNA methylation and other localized epigenetic marks act as symbolic 'on/off' signals, while repetitive and noncoding DNA elements form a substrate for probabilistic, distributed responses to environmental stimuli. This hybrid perspective parallels machine-learning approaches, where symbolic representations are combined with subsymbolic methods (e.g. neural networks) to achieve robust learning and adaptation. Here, we propose that epigenetic regulation integrates these two dimensions (i.e. symbolic control and subsymbolic redundancy) to enable cells to adapt to complex environmental challenges, maintain heritable memory of past exposures, and evolve. In this manuscript, we introduce the concept of epigenetic intelligence, clarifying the synergy between discrete, 'symbolic' epigenetic switches (e.g. gene-specific DNA methylation) and the more 'subsymbolic', distributed features of the genome (e.g. repetitive elements methylation). This approach appears to be novel, as existing literature has not explicitly framed epigenetic regulation within a neuro-symbolic artificial intelligence perspective.