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cs.NE2025
Structural Plasticity as Active Inference: A Biologically-Inspired Architecture for Homeostatic Control
Brennen A. Hill
Traditional neural networks, while powerful, rely on biologically implausible learning mechanisms such as global backpropagation. This paper introduces the Structurally Adaptive Pr…
cs.NE2025
The Physical Basis of Prediction: World Model Formation in Neural Organoids via an LLM-Generated Curriculum
Brennen Hill
The capacity of an embodied agent to understand, predict, and interact with its environment is fundamentally contingent on an internal world model. This paper introduces a novel fr…
cs.NE2025★ 1 cited
The Geometry of Cortical Computation: Manifold Disentanglement and Predictive Dynamics in VCNet
Brennen A. Hill, Zhang Xinyu, Timothy Putra Prasetio
Despite their success, modern convolutional neural networks (CNNs) exhibit fundamental limitations, including data inefficiency, poor out-of-distribution generalization, and vulner…