ReviewNature reviews. Neuroscience2021
Neural tuning and representational geometry.
Review in Nature reviews. Neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers.
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123 citing papers in PubMed, 194 citations in OpenAlex.
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- 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
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- Artificial neural manifolds.Nature communications · 2026Article
- Computation embodied in population geometry.Nature communications · 2026Article
- The human language processing system straightens natural speech.bioRxiv : the preprint server for biology · 2026Article
- Stable readout of visual representations mediates flexible generalization.Nature communications · 2026Article
- Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning.Communications psychology · 2026Article
- Cortical neuron classes and recursive curvature collapse: a neurobiological model of conscious dynamics.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Experience shapes the transformation of olfactory representations along the cortico-hippocampal pathway.eLife · 2026Article
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- Towards a Mathematical Structure of Global Phenomenal Consciousness.Entropy (Basel, Switzerland) · 2026Article
- Cerebellar perturbation impairs human working memory and degrades spatial tuning throughout cortex.bioRxiv : the preprint server for biology · 2026Article
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- The cost of efficiency in flexible neural representations.bioRxiv : the preprint server for biology · 2026Article
63 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.
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