arXiv subjects

Luis F Seoane

Publications and source records attributed to Luis F Seoane.

4 recordsLinked to original sources

arXiv2026

Cognition spaces: natural, artificial, and hybrid

Cognitive processes are realized across an extraordinary range of natural, artificial, and hybrid systems, yet there is no unified framework for comparing their forms, limits, and unrealized possibilities. Here, we propose a cognition space approach that replaces narrow, substrate-dependent definitions with a comparative representation based on organizational and informational dimensions. Within this framework, cognition is treated as a graded capacity to sense, process, and act upon information, allowing systems as diverse as cells, brains, artificial agents, and human-AI collectives to be analyzed within a common conceptual landscape. We introduce and examine three cognition spaces -- basal aneural, neural, and human-AI hybrid -- and show that their occupation is highly uneven, with clusters of realized systems separated by large unoccupied regions. We argue that these voids are not accidental but reflect evolutionary contingencies, physical constraints, and design limitations. By focusing on the structure of cognition spaces rather than on categorical definitions, this approach clarifies the diversity of existing cognitive systems and highlights hybrid cognition as a promising frontier for exploring novel forms of complexity beyond those produced by biological evolution.

q-bio.NC↗

arXiv2025

Topology of Syntax Networks across Languages

Syntax connects words to each other in very specific ways. Two words are syntactically connected if they depend directly on each other. Syntactic connections usually happen within a sentence. Gathering all those connection across several sentences gives birth to syntax networks. Earlier studies in the field have analysed the structure and properties of syntax networks trying to find clusters/phylogenies of languages that share similar network features. The results obtained in those studies will be put to test in this thesis by increasing both the number of languages and the number of properties considered in the analysis. Besides that, language networks of particular languages will be inspected in depth by means of a novel network analysis [25]. Words (nodes of the network) will be clustered into topological communities whose members share similar features. The properties of each of these communities will be thoroughly studied along with the Part of Speech (grammatical class) of each word. Results across different languages will also be compared in an attempt to discover universally preserved structural patterns across syntax networks.

cs.CL↗

arXiv2024

Hierarchical genotype networks and incipient ecological speciation in Q$β$ phage quasispecies

Understanding how viral mutant spectra organize and explore genotype space is essential for unraveling the mechanisms driving evolution at the finest scale. Here we use deep-sequencing data of an amplicon in the A2 protein of the RNA bacteriophage Q$β$ to reconstruct genotype networks with tens of thousands of different haplotypes. The study of populations evolved under different temperature regimes uncovers generic topological features conditioned by fundamental structural motifs of genotype networks -- tetrahedrons, triangles, and squares -- that govern their local architecture. Mutant swarms display a hierarchical structure where sequences cluster around a highly connected and abundant sequence core that sustains population diversity. The immediate neighborhood of this core is comprehensively sampled, with no signs of selection, while a few mutations away sampling becomes dynamical and sparse, showing signs of purifying selection. By aggregating genotype networks from populations adapted to different temperatures, we capture the early stages of evolutionary divergence, with overlapping populations that remain connected through short mutational paths. Even at the time scale of these experiments, evolutionary pathways might be multiple, preventing the backward reconstruction of unique trajectories once mutations have been fixed. This analysis provides a detailed view of the local, fine-scale processes shaping viral quasispecies evolution and underscores the usefulness of genotype networks as an enlightening visualization of the organization of mutant swarms.

q-bio.PE↗

arXiv2024

Topological communities in complex networks

Most complex systems can be captured by graphs or networks. Networks connect nodes (e.g.\ neurons) through edges (synapses), thus summarizing the system's structure. A popular way of interrogating graphs is community detection, which uncovers sets of geometrically related nodes. {\em Geometric communities} consist of nodes ``closer'' to each other than to others in the graph. Some network features do not depend on node proximity -- rather, on them playing similar roles (e.g.\ building bridges) even if located far apart. These features can thus escape proximity-based analyses. We lack a general framework to uncover such features. We introduce {\em topological communities}, an alternative perspective to decomposing graphs. We find clusters that describe a network as much as classical communities, yet are missed by current techniques. In our framework, each graph guides our attention to its relevant features, whether geometric or topological. Our analysis complements existing ones, and could be a default method to study networks confronted without prior knowledge. Classical community detection has bolstered our understanding of biological, neural, or social systems; yet it is only half the story. Topological communities promise deep insights on a wealth of available data. We illustrate this for the global airport network, human connectomes, and others.

physics.soc-ph↗