Intelligent Neural Networks: From Layered Architectures to Graph-Organized Intelligence

• URL: http://arxiv.org/abs/2511.22813v1
• Date: Thu, 27 Nov 2025 23:59:29 GMT
• Title: Intelligent Neural Networks: From Layered Architectures to Graph-Organized Intelligence
• Authors: Antoine Salomon,
• Abstract summary: We introduce Intelligent Neural Networks (INN), a paradigm shift where neurons are first-class entities with internal memory and learned communication patterns.<n>On the standard Text8 character modeling benchmark, INN achieves 1.705 Bit-Per-Character (BPC)<n>This work demonstrates that neuron-centric design with graph organization is not merely bio-inspired -- it is computationally effective.
• Score: 0.20305676256390928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biological neurons exhibit remarkable intelligence: they maintain internal states, communicate selectively with other neurons, and self-organize into complex graphs rather than rigid hierarchical layers. What if artificial intelligence could emerge from similarly intelligent computational units? We introduce Intelligent Neural Networks (INN), a paradigm shift where neurons are first-class entities with internal memory and learned communication patterns, organized in complete graphs rather than sequential layers. Each Intelligent Neuron combines selective state-space dynamics (knowing when to activate) with attention-based routing (knowing to whom to send signals), enabling emergent computation through graph-structured interactions. On the standard Text8 character modeling benchmark, INN achieves 1.705 Bit-Per-Character (BPC), significantly outperforming a comparable Transformer (2.055 BPC) and matching a highly optimized LSTM baseline. Crucially, a parameter-matched baseline of stacked Mamba blocks fails to converge (>3.4 BPC) under the same training protocol, demonstrating that INN's graph topology provides essential training stability. Ablation studies confirm this: removing inter-neuron communication degrades performance or leads to instability, proving the value of learned neural routing. This work demonstrates that neuron-centric design with graph organization is not merely bio-inspired -- it is computationally effective, opening new directions for modular, interpretable, and scalable neural architectures.

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