ReviewTrends in cognitive sciences2024
Computational role of structure in neural activity and connectivity.
Review in Trends in cognitive sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Learning shapes neural geometry in the primate prefrontal cortex.Nature neuroscience · 2026Article
- Developmental tuning of functional manifold dimensionality across the human brain.bioRxiv : the preprint server for biology · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Brain-inspired strategies for efficient artificial intelligence.Molecules and cells · 2026Review
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- A complex network perspective on brain disease.Biological reviews of the Cambridge Philosophical Society · 2026Review
- The geometry of context-dependent biased decisions during learning.bioRxiv : the preprint server for biology · 2026Article
- Efficient and dynamic neural geometry of value and modality encoding in the primate putamen for value-guided behavior.Nature communications · 2025Article
- ACC representations of reward-driven motivation over hierarchically-organized behavior.NeuroImage · 2025Article
- Article
- Dynamic coding and sequential integration of multiple reward attributes by primate amygdala neurons.Nature communications · 2025Article
- Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy.bioRxiv : the preprint server for biology · 2025Article
- Linking neural population formatting to function.bioRxiv : the preprint server for biology · 2025Article
- Convergent representation of values from tactile and visual inputs for efficient goal-directed behavior in the primate putamen.Nature communications · 2024Article
- Modular representations emerge in neural networks trained to perform context-dependent tasks.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
One major challenge of neuroscience is identifying structure in seemingly disorganized neural activity. Different types of structure have different computational implications that can help neuroscientists understand the functional role of a particular brain area. Here, we outline a unified approach to characterize structure by inspecting the representational geometry and the modularity properties of the recorded activity and show that a similar approach can also reveal structure in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent studies of model networks performing three classes of computations.
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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.