A rate RNN trained on a context-dependent decision-making task spontaneously fo…

{
  "n": 0,
  "doi": "10.1016/j.isci.2021.102919",
  "claim": "A rate RNN trained on a context-dependent decision-making task spontaneously forms line attractors in low-dimensional subspaces to integrate sensory evidence after maintaining context cues with stable low-activity persistent states.",
  "cite_key": "Zhang2021b",
  "evidence": "Rate RNN trained on context-dependent decision-making and analyzed with dynamic epoch-wise PCA; compared to rodent and monkey PFC features.",
  "effect_size": "qualitative",
  "text_access": "abstract_only",
  "study_system": "rate-based recurrent neural network model, compared to rodent and monkey PFC",
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  "claim_source_sentence": "In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision-making.",
  "source_provenance_status": "non_substring_match",
  "replication_evidence_dois": [
    "10.1038/nature12742",
    "10.1038/nn.4244"
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In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision-making.

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