Autoplastic neural network: self-evolving networks with dynamic connectivity and modality adaptation

Abstract Deep networks are usually built first and trained later, forcing fixed capacity and wasted compute. We introduce the Autoplastic Neural Network, a training-time framework that learns structure and weights together. Layer inclusion, neuron-level connectivity, and per-layer operation mixing are jointly optimized within a fixed maximal supernetwork, whose pre-allocated components are reinforced or pruned as learning progresses; the resulting model supports per-input routing and can also be exported as a compact static architecture. A stability-gated rule coordinates architectural changes with gradient descent and yields convergence guarantees. Across vision, video, language, bioinformatics, and healthcare tasks, the approach surpasses strong static and dynamic baselines while using less computation. Unlike neural architecture search, no outer search loop is required; evolution happens inside a single learning process with cost comparable to training a single model. By unifying optimization and design, Autoplastic networks allocate capacity where needed, remove redundancy when possible, and deliver practical, resource-aware deep learning.