Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems

• URL: http://arxiv.org/abs/2601.14096v1
• Date: Tue, 20 Jan 2026 15:57:36 GMT
• Title: Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems
• Authors: Benedikt Hartl, Léo Pio-Lopez, Chris Fields, Michael Levin,
• Abstract summary: The emerging field of diverse intelligence seeks an integrated view of problem-solving in agents of very different composition, composition, and substrates.<n>We propose that cognition in both natural and synthetic systems can be characterized and understood by the interplay between two equally important invariants.
• Score: 1.7499351967216341
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The emerging field of diverse intelligence seeks an integrated view of problem-solving in agents of very different provenance, composition, and substrates. From subcellular chemical networks to swarms of organisms, and across evolved, engineered, and chimeric systems, it is hypothesized that scale-invariant principles of decision-making can be discovered. We propose that cognition in both natural and synthetic systems can be characterized and understood by the interplay between two equally important invariants: (1) the remapping of embedding spaces, and (2) the navigation within these spaces. Biological collectives, from single cells to entire organisms (and beyond), remap transcriptional, morphological, physiological, or 3D spaces to maintain homeostasis and regenerate structure, while navigating these spaces through distributed error correction. Modern Artificial Intelligence (AI) systems, including transformers, diffusion models, and neural cellular automata enact analogous processes by remapping data into latent embeddings and refining them iteratively through contextualization. We argue that this dual principle - remapping and navigation of embedding spaces via iterative error minimization - constitutes a substrate-independent invariant of cognition. Recognizing this shared mechanism not only illuminates deep parallels between living systems and artificial models, but also provides a unifying framework for engineering adaptive intelligence across scales.

Related papers

• Conditional Morphogenesis: Emergent Generation of Structural Digits via Neural Cellular Automata [0.0]
  We propose a novel Conditional Neural Cellular Automata architecture capable of growing distinct topological structures from a single generic seed.<n>By injecting a one-hot condition into the cellular perception field, a single set of local rules can learn to break symmetry and self-assemble into ten distinct geometric attractors.
  arXiv  Detail & Related papers  (2025-12-09T08:36:54Z)
• Neural cellular automata: applications to biology and beyond classical AI [1.9116784879310027]
  Neural Cellular Automata (NCA) represent a powerful framework for modeling biological self-organization.<n>NCA can simulate processes across molecular, cellular, tissue, and system-level scales.<n>Their governing self-regulatory behavior is constraint to fully localized interactions, yet their collective behavior scales into coordinated system-level outcomes.
  arXiv  Detail & Related papers  (2025-09-14T06:55:29Z)
• Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective [53.556348738917166]
  Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning.<n>Human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments.
  arXiv  Detail & Related papers  (2025-09-11T05:23:22Z)
• Controllable diffusion-based generation for multi-channel biological data [66.44042377817074]
  This work proposes a unified diffusion framework for controllable generation over structured and spatial biological data.<n>We show state-of-the-art performance across both spatial and non-spatial prediction tasks, including protein imputation in IMC and gene-to-protein prediction in single-cell datasets.
  arXiv  Detail & Related papers  (2025-06-24T00:56:21Z)
• Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View [49.03501451546763]
  We identify the importance of implicit correspondences across biological contexts for exploiting domain-invariant pathological composition. We propose self-adaptive dynamic distillation to secure instance-aware trade-offs across different model constituents.
  arXiv  Detail & Related papers  (2024-07-14T04:41:16Z)
• Engineering morphogenesis of cell clusters with differentiable programming [2.0690546196799042]
  We use recent advances in automatic differentiation to discover local interaction rules and genetic networks.<n>We show that one can learn the parameters governing cell interactions in the form of interpretable genetic networks.
  arXiv  Detail & Related papers  (2024-07-08T18:05:11Z)
• Closing the Loop: How Semantic Closure Enables Open-Ended Evolution? [0.042970700836450486]
  This manuscript explores the evolutionary emergence of semantic closure.<n>It integrates concepts from biology, physical biosemiotics, and ecological psychology.<n>We develop a model capable of capturing critical properties of life, including autopoiesis, anticipation, and adaptation.
  arXiv  Detail & Related papers  (2024-04-05T19:35:38Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture [0.0]
  We provide an overview of the hallmarks endowing biological neural networks with the functionality needed for flexible cognition. As machine learning models become more complex, these principles may provide valuable directions in an otherwise vast space of possible architectures.
  arXiv  Detail & Related papers  (2023-03-21T18:36:17Z)
• Growing Isotropic Neural Cellular Automata [63.91346650159648]
  We argue that the original Growing NCA model has an important limitation: anisotropy of the learned update rule. We demonstrate that cell systems can be trained to grow accurate asymmetrical patterns through either of two methods.
  arXiv  Detail & Related papers  (2022-05-03T11:34:22Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning [0.0]
  We propose a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.
  arXiv  Detail & Related papers  (2020-04-11T21:02:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.