The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Representational geometry: integrating cognition, computation, and the brain
Open accessAbstract
The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure.
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Cites
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Self-citationNeuronal Population Coding of Movement Direction
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Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204
Self-citationKriegeskorte, N. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035447 · Cited by 1,093
Self-citationA Toolbox for Representational Similarity Analysis
Nili, H., Wingfield, C., Walther, A., Su, L., Marslen-Wilson, W., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003553 · Cited by 937
Self-citation‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework
Stokes, M. G. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.05.004 · Cited by 850
Metadata
- Authors
- Kriegeskorte, N., & Kievit, R. A.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 17
- Issue
- 8
- Pages
- 401-412
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2153633111
- Cited by
- 911
- References
- 113
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
