The population doctrine in cognitive neuroscience
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Abstract (arXiv)
A major shift is happening within neurophysiology: a population doctrine is drawing level with the single-neuron doctrine that has long dominated the field. Population-level ideas have so far had their greatest impact in motor neuroscience, but they hold great promise for resolving open questions in cognition as well. Here, we codify the population doctrine and survey recent work that leverages this view to specifically probe cognition. Our discussion is organized around five core concepts that provide a foundation for population-level thinking: (1) state spaces, (2) manifolds, (3) coding dimensions, (4) subspaces, and (5) dynamics. The work we review illustrates the progress and promise that population neurophysiology holds for cognitive neuroscience-for delivering new insight into attention, working memory, decision-making, executive function, learning, and reward processing.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 0; 2026: 2.
7 papers.
- 2022OpenApePose: a database of annotated ape photographs for pose estimationcited by 10
- 2023Identifying Interpretable Visual Features in Artificial and Biological Neural Systemscited by 9
- 2024Choosing the right basis for interpretability: Psychophysical comparison between neuron-based and dictionary-based representationscited by 5
- 2026Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifoldscited by 3
- 2021Emergent behavior and neural dynamics in artificial agents tracking turbulent plumescited by 2
- 2023Inferring Inferencecited by 1
- 2026Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplingscited by 0
- 2026Self-Organized Learning in Oscillatory Neural Networks with Memristive Signed Couplings1 Jul 2026
- 2026Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds23 Jun 2026
- 2024Choosing the right basis for interpretability: Psychophysical comparison between neuron-based and dictionary-based representations6 Nov 2024
- 2023Identifying Interpretable Visual Features in Artificial and Biological Neural Systems17 Oct 2023
- 2023Inferring Inference4 Oct 2023
- 2022OpenApePose: a database of annotated ape photographs for pose estimation30 Nov 2022
- 2021Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes25 Sep 2021