[2602.23405] On De-Individuated Neurons: Continuous Symmetries Enable Dynamic Topologies

Computer Science > Neural and Evolutionary Computing arXiv:2602.23405 (cs) [Submitted on 26 Feb 2026] Title:On De-Individuated Neurons: Continuous Symmetries Enable Dynamic Topologies Authors:George Bird View a PDF of the paper titled On De-Individuated Neurons: Continuous Symmetries Enable Dynamic Topologies, by George Bird View PDF HTML (experimental) Abstract:This paper introduces a novel methodology for dynamic networks by leveraging a new symmetry-principled class of primitives, isotropic activation functions. This approach enables real-time neuronal growth and shrinkage of the architectures in response to task demand. This is made possible by network structural changes that are invariant under symmetry reparameterisations, leaving the computation identical under neurogenesis and well approximated under neurodegeneration. This is undertaken by leveraging the isotropic primitives' property of basis independence, resulting in the loss of the individuated neurons implicit in the elementwise functional form. Isotropy thereby allows a freedom in the basis to which layers are decomposed and interpreted as individual artificial neurons. This enables a layer-wise diagonalisation procedure, in which typical interconnected layers, such as dense layers, convolutional kernels, and others, can be reexpressed so that neurons have one-to-one, ordered connectivity within alternating layers. This indicates which one-to-one neuron-to-neuron communications are strongly impactful on over...