arXiv Machine Learning
Isotropic Activation Functions Enable Deindividuated Neurons and Adaptive Topologies
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arXiv Machine Learning
Jun 4
Beyond Structural Symmetries: Linear Mode Connectivity via Neuron Identifiability
arXiv:2606. 04754v1 Announce Type: new Abstract: Many striking phenomena in deep learning, such as linear mode connectivity and the structured behavior of training dynamics, are closely tied to parameter symmetries: transformations that leave the realized function unchanged.
arXiv Machine Learning
Oct 2
Parameter symmetries determine representational geometry in overparameterized nonlinear networks
The paper investigates how parameter symmetries in overparameterized nonlinear neural networks affect representational geometry. It shows that these symmetries can be described by three basic feature transformations—addition, duplication, and scaling—allowing a closed‑form decomposition of representations into essential and auxiliary parts. The study further demonstrates that implementation‑level selection rules can resolve the resulting degeneracy, producing identifiable geometries that reflect feature contributions to the network’s function.
arXiv AI
Jun 2
Rare Events, Real Signals: Functional Ensembles as Units of Computation in Deep Spiking Networks
arXiv:2606. 00073v1 Announce Type: cross Abstract: We investigate how internal representations emerge across hierarchical processing systems by introducing a neuroscience-inspired framework for analyzing deep spiking neural networks (SNN) through the lens of functional connectivity.
arXiv Machine Learning
Oct 2
Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks
The paper investigates the relationship between representation and function in neural networks, using analytical two‑layer linear models and simulations of nonlinear networks. It demonstrates that functional similarity and representational similarity can be dissociated: networks may share representations without sharing functions, and vice versa. The study also finds that robustness to input noise or generalization error does not constrain representations, whereas robustness to parameter noise forces networks to adopt task‑specific representations, indicating that representational alignment reflects computational advantages beyond mere functional alignment.
arXiv AI
Jun 16
Harnessing cortical geometry, wiring, and function as inductive biases for recurrent neural networks
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
arXiv Machine Learning
Sep 15
The Metric Slingshot: Navigational Reuse as Width-Optimal Structural Decoupling in Continual Learning
arXiv:2603.15412v2 Announce Type: replace Abstract: The mammalian brain, most extensively studied in rodents and bats, solves an enormous variety of non-spatial cognitive tasks using neural circuitry...