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Cited by 191

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Neuronal Population Coding of Parametric Working Memory

Open access
Barak, O., Tsodyks, M., Romo, R.·2010·Journal of Neuroscience

Abstract

Comparing two sequentially presented stimuli is a widely used experimental paradigm for studying working memory. The delay activity of many single neurons in the prefrontal cortex (PFC) of monkeys was found to be stimulus-specific, however, population dynamics of stimulus representation has not been elucidated. We analyzed the population state of a large number of PFC neurons during a somatosensory discrimination task. Using the tuning curves of the neurons, we derived a compact characterization of the population state. Stimulus representation by the population was found to degrade after stimulus termination, and emerge in a different form toward the end of the delay. Specifically, the tuning properties of neurons were found to change during the task. We suggest a mechanism whereby information about the stimulus is contained in activity-dependent synaptic facilitation of recurrent connections.

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Crossref · 10.1523/jneurosci.1875-10.2010

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Metadata

Authors
Barak, O., Tsodyks, M., & Romo, R.
Venue
Journal of Neuroscience
Published
2010-07-14
Volume
30
Issue
28
Pages
9424-9430
ISSN
  • 0270-6474 (ISSN-L)
  • 1529-2401
OpenAlex
W2097996618
Cited by
191
References
6
FWCI
4.89
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Neuronal Population Coding of Parametric Working Memory. https://app.cochat.ai/papers/10.1523/jneurosci.1875-10.2010
The paper
Barak, O., Tsodyks, M., & Romo, R. (2010). Neuronal Population Coding of Parametric Working Memory. Journal of Neuroscience. https://doi.org/10.1523/jneurosci.1875-10.2010

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.