ArticleAdvances in neural information processing systems2021
Generalized Shape Metrics on Neural Representations.
Article in Advances in neural information processing systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment.Neural computation · 2026Article
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- Low rank adaptation of chemical foundation models generates effective odorant representations.bioRxiv : the preprint server for biology · 2026Article
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- Quantifying task-relevant representational similarity using decision variable correlation.ArXiv · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- From Signal Quality to Representation Stability in Textile Bioelectronic Interfaces.BME frontiers · 2026Article
- PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learning.Proceedings of Machine Learning and Systems ... · 2026Article
- InputDSA : Demixing then comparing recurrent and externally driven dynamics.... International Conference on Learning Representations · 2026Article
- Graph embeddings for identifying symmetries in Connectomes.bioRxiv : the preprint server for biology · 2025Article
- Quantifying Task-relevant Similarities in Representations Using Decision Variable Correlations.Advances in neural information processing systems · 2025Article
- Review
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- Source-Free Model Transferability Assessment for Smart Surveillance via Randomly Initialized Networks.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Understanding the operation of biological and artificial networks remains a difficult and important challenge. To identify general principles, researchers are increasingly interested in surveying large collections of networks that are trained on, or biologically adapted to, similar tasks. A standardized set of analysis tools is now needed to identify how network-level covariates-such as architecture, anatomical brain region, and model organism-impact neural representations (hidden layer activations). Here, we provide a rigorous foundation for these analyses by defining a broad family of metric spaces that quantify representational dissimilarity. Using this framework, we modify existing representational similarity measures based on canonical correlation analysis and centered kernel alignment to satisfy the triangle inequality, formulate a novel metric that respects the inductive biases in convolutional layers, and identify approximate Euclidean embeddings that enable network representations to be incorporated into essentially any off-the-shelf machine learning method. We demonstrate these methods on large-scale datasets from biology (Allen Institute Brain Observatory) and deep learning (NAS-Bench-101). In doing so, we identify relationships between neural representations that are interpretable in terms of anatomical features and model performance.
Identifiers
38170102PMC10760997What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.