ReviewNeuron2024
Mixed selectivity: Cellular computations for complexity.
Review in Neuron, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed.
- Purkinje Cells in Crus I and II Encode the Visual Stimulus and the Impending Choice as Monkeys Learn a Reinforcement-Based Visuomotor Association Task.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Cannabinoid modulation of central amygdala population dynamics during threat investigation.Nature communications · 2026Article
- Hippocampal Local Field Potentials Encode Continuous Flight Speed in Homing Pigeons via Complementary Gamma and Theta Signatures.Animals : an open access journal from MDPI · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Stable readout of visual representations mediates flexible generalization.Nature communications · 2026Article
- Compositionality of social gaze in the prefrontal-amygdala circuits.Nature communications · 2026Article
- The Laminar Organization of a Decision Circuit in Orbitofrontal Cortex is Agnostic to the Variable Encoding Scheme.bioRxiv : the preprint server for biology · 2026Article
- Categorization is 'baked' into the brain.Nature reviews. Neuroscience · 2026Review
- Article
- Dopamine D1 and D2 receptors differentially control strength and dynamics of abstract decision codes in the primate prefrontal cortex.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The Compositional Encoding of Hand-Eye Coordinated Movements for Single Neurons in the Posterior Parietal Cortex.bioRxiv : the preprint server for biology · 2026Article
- Functional bipartition of medial prefrontal cortex into salience detection and movement gain.bioRxiv : the preprint server for biology · 2026Article
- Neural geometry in the human hippocampus enables generalization across spatial position and gaze.ArXiv · 2026Article
- Article
- Article
- Rethinking the centrality of brain areas in understanding functional organization.Nature neuroscience · 2026Review
- Accumbal calcium-permeable AMPA receptors orchestrate neuronal ensembles underlying social attachment.Nature communications · 2026Article
- Brain-computer interfaces: an engineering black-box swindle or a lone advance guided by deep learning.Frontiers in neuroscience · 2026Article
- Convergence-divergence circuits for multimodal integration of innate and learned opponent valences.Frontiers in systems neuroscience · 2026Article
- Optogenetic interrogation of the zebrafish lateral line reveals brain-wide neural circuits involved in pattern separation.iScience · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The property of mixed selectivity has been discussed at a computational level and offers a strategy to maximize computational power by adding versatility to the functional role of each neuron. Here, we offer a biologically grounded implementational-level mechanistic explanation for mixed selectivity in neural circuits. We define pure, linear, and nonlinear mixed selectivity and discuss how these response properties can be obtained in simple neural circuits. Neurons that respond to multiple, statistically independent variables display mixed selectivity. If their activity can be expressed as a weighted sum, then they exhibit linear mixed selectivity; otherwise, they exhibit nonlinear mixed selectivity. Neural representations based on diverse nonlinear mixed selectivity are high dimensional; hence, they confer enormous flexibility to a simple downstream readout neural circuit. However, a simple neural circuit cannot possibly encode all possible mixtures of variables simultaneously, as this would require a combinatorially large number of mixed selectivity neurons. Gating mechanisms like oscillations and neuromodulation can solve this problem by dynamically selecting which variables are mixed and transmitted to the readout.
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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.