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"n": null,
"doi": "10.1038/s41586-019-0997-6",
"claim": "Single-neuron perturbations reveal feature-specific competition in V1. Here we describe a method-influence mapping-that uses single-neuron perturbations to directly measure how cortical neurons reshape sensory representations.",
"cite_key": "Chettih2019",
"evidence": "Single-neuron perturbations reveal feature-specific competition in V1. — The computations performed by local neural populations, such as a cortical layer, are typically inferred from anatomical connectivity and observations of neural activity. Here we describe a method-influence mapping-that uses single-neuron perturbations to directly measure how cortical neurons r",
"effect_size": null,
"text_access": "fulltext",
"study_system": "mouse cortex (see paper for region/cell-type detail)",
"argument_role": "supporting",
"replication_status": "single_study",
"claim_source_sentence": "We used this approach because there were correlations between metrics, such as higher activity correlations at shorter anatomical distances and a positive correlation between signal and noise correlations (–).",
"source_provenance_status": "ok",
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"effect_size_source_sentence": null
}- source_refs
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"paper:paper-31f54bcea8a4"
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"notes": [
"Local review repositories are read-only inputs.",
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Single-neuron perturbations reveal feature-specific competition in V1. — The computations performed by local neural populations, such as a cortical layer, are typically inferred from anatomical connectivity and observations of neural activity. Here we describe a method-influence mapping-that uses single-neuron perturbations to directly measure how cortical neurons r