Version history

{
  "scope": "mouse cortex (see paper for region/cell-type detail)",
  "claim_text": "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.",
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    "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.",
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    "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",
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    "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 (–).",
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  "section_id": "section_11",
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  "study_system": "mouse cortex (see paper for region/cell-type detail)",
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  "section_title": "11. Physiological signature III — pattern completion, replay, and sequence generation as recurrent-circuit read-outs in mouse cortex",
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  "evidence_summary": "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",
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}