[2603.03234] Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations

Computer Science > Machine Learning arXiv:2603.03234 (cs) [Submitted on 3 Mar 2026] Title:Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations Authors:Patrick Inoue, Florian Röhrbein, Andreas Knoblauch View a PDF of the paper titled Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations, by Patrick Inoue and 1 other authors View PDF HTML (experimental) Abstract:While deep neural networks (DNNs) have achieved remarkable performance in tasks such as image recognition, they often struggle with generalization, learning from few examples, and continuous adaptation - abilities inherent in biological neural systems. These challenges arise due to DNNs' failure to emulate the efficient, adaptive learning mechanisms of biological networks. To address these issues, we explore the integration of neurobiologically inspired assumptions in neural network learning. This study introduces a biologically inspired learning rule that naturally integrates neurobiological principles, including sparsity, lognormal weight distributions, and adherence to Dale's law, without requiring explicit enforcement. By aligning with these core neurobiological principles, our model enhances robustness against adversarial attacks and demonstrates superior generalization, particularly in few-shot learning scenarios. Notably, integrating these constraints leads to the emergence of biologically plaus...