ReviewCurrent opinion in neurobiology2021
Neural population geometry: An approach for understanding biological and artificial neural networks.
Review in Current opinion in neurobiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
133 citing papers in PubMed.
- Neural and perceptual manifolds regarded geometrically.Journal of vision · 2026Review
- Behavioral demands organize a decision process into distinct yet coordinated neural representations in parietal cortex.bioRxiv : the preprint server for biology · 2026Article
- Representational learning by optimization of neural manifolds in an olfactory memory network.Nature neuroscience · 2026Article
- Feature interference underlies a neuronal basis for the behavioral cost of task uncertainty.Nature neuroscience · 2026Article
- Loss of neuronal population organization links pathology to behavior in a model of Alzheimer's disease.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Memory encoding reprograms neuronal transcriptional responses via durable chromatin remodeling.bioRxiv : the preprint server for biology · 2026Article
- The representational geometry of emotional states in basolateral amygdala.Nature neuroscience · 2026Article
- Article
- Primary cilia and neural computation.Journal of biomedical science · 2026Review
- Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026Review
- Emergent representations of graphical structure in mechanistic neural models of causal judgment.bioRxiv : the preprint server for biology · 2026Article
- Representation Transfer via Invariant Input-driven Continuous Attractors for Fast Domain Adaptation.Communications biology · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- Manifolds in the Medial Premotor Cortex During Switching From Attending to Tapping to a Metronome.Annals of the New York Academy of Sciences · 2026Article
- Loss of neuronal population organization links pathology to behavior in a model of Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Neural geometry in the human hippocampus enables generalization across spatial position and gaze.ArXiv · 2026Article
- Neural population geometry and optimal coding of tasks with shared latent structure.Nature neuroscience · 2026Article
- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Rethinking the centrality of brain areas in understanding functional organization.Nature neuroscience · 2026Review
73 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Advances in experimental neuroscience have transformed our ability to explore the structure and function of neural circuits. At the same time, advances in machine learning have unleashed the remarkable computational power of artificial neural networks (ANNs). While these two fields have different tools and applications, they present a similar challenge: namely, understanding how information is embedded and processed through high-dimensional representations to solve complex tasks. One approach to addressing this challenge is to utilize mathematical and computational tools to analyze the geometry of these high-dimensional representations, i.e., neural population geometry. We review examples of geometrical approaches providing insight into the function of biological and artificial neural networks: representation untangling in perception, a geometric theory of classification capacity, disentanglement, and abstraction in cognitive systems, topological representations underlying cognitive maps, dynamic untangling in motor systems, and a dynamical approach to cognition. Together, these findings illustrate an exciting trend at the intersection of machine learning, neuroscience, and geometry, in which neural population geometry provides a useful population-level mechanistic descriptor underlying task implementation. Importantly, geometric descriptions are applicable across sensory modalities, brain regions, network architectures, and timescales. Thus, neural population geometry has the potential to unify our understanding of structure and function in biological and artificial neural networks, bridging the gap between single neurons, population activities, and behavior.
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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.