ArticleNeuron2022
Orthogonal representations for robust context-dependent task performance in brains and neural networks.
Article in Neuron, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
- Network geometry shapes the transformation of task representations across the human cortex.Nature neuroscience · 2026Article
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- Specific expertise from diffuse feedback: A feedforward-gated mechanism.Science advances · 2026Article
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- Spontaneous emergence of context-dependent statistical learning in humans and neural networks.iScience · 2026Article
- Sensorimotor remapping drives task specialization in prefrontal cortex.Nature communications · 2026Article
- Learning shapes neural geometry in the primate prefrontal cortex.Nature neuroscience · 2026Article
- Hippocampal engrams configure prefrontal context representations to guide flexible decisions.bioRxiv : the preprint server for biology · 2026Article
- Low-dimensional and optimised representations of high-level information in the expert brain.Nature communications · 2026Article
- Structure, disorder, and dynamics in task-trained recurrent neural circuits.bioRxiv : the preprint server for biology · 2026Article
- Steering through task uncertainty: Evidence for activation modulation over dynamic binding.Psychonomic bulletin & review · 2026Article
- Neural population geometry and optimal coding of tasks with shared latent structure.Nature neuroscience · 2026Article
- Entorhinal Cortex Signals Dimensions of Past Experience That Can Be Generalized in a Novel Environment.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Sensory integration, temporal prediction, and rule discovery reflect interdependent inference processes.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Order parameters and phase transitions of continual learning in deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Domain-specific schema reuse supports flexible learning to learn in the primate brain.Nature communications · 2026Article
- Orthogonal-Rotational Dynamics Supports Efficient Encoding and Updating for Streaming Information in Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Humans and neural networks show similar patterns of transfer and interference during continual learning.Nature human behaviour · 2026Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
Abstract
How do neural populations code for multiple, potentially conflicting tasks? Here we used computational simulations involving neural networks to define "lazy" and "rich" coding solutions to this context-dependent decision-making problem, which trade off learning speed for robustness. During lazy learning the input dimensionality is expanded by random projections to the network hidden layer, whereas in rich learning hidden units acquire structured representations that privilege relevant over irrelevant features. For context-dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds. Using behavioral testing and neuroimaging in humans and analysis of neural signals from macaque prefrontal cortex, we report evidence for neural coding patterns in biological brains whose dimensionality and neural geometry are consistent with the rich learning regime.
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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.