Competing predictive coding models: SST role in prediction vs error computation

Models disagree on whether SST neurons carry the prediction signal (top-down expectations mediated through dendritic inhibition) or contribute to computing the prediction error. This reveals a fundamental unresolved question about SST's role in cortical inference.

{
  "papers": [
    {
      "doi": "10.7554/elife.57541",
      "value": "SST carries prediction signal — inhibitory plasticity learns to predict expected input",
      "method": "inhibitory plasticity learning rule",
      "metric": "SST role in predictive coding",
      "cite_key": "Hertag2020",
      "condition": "sensory prediction",
      "study_system": "mean-field canonical circuit",
      "value_source_sentence": "Learning prediction error neurons in a canonical interneuron circuit."
    },
    {
      "doi": "10.7554/elife.95127",
      "value": "SST signals predictions with uncertainty modulation of prediction error magnitude",
      "method": "uncertainty estimation",
      "metric": "SST role in predictive coding",
      "cite_key": "Wilmes2025",
      "condition": "Bayesian inference",
      "study_system": "microcircuit model",
      "value_source_sentence": "Uncertainty-modulated prediction errors in cortical microcircuits."
    },
    {
      "doi": "10.1101/2025.11.01.686040",
      "value": "SOM provides compartment-specific inhibition for sign-specific PE without learning",
      "method": "fixed-weight spiking dynamics",
      "metric": "SST role in predictive coding",
      "cite_key": "Nemati2025",
      "condition": "V1 L2/3 predictive coding",
      "study_system": "spiking network, 2-compartment pyramidal",
      "value_source_sentence": "A spiking network model is presented here in which two-compartment excitatory pyramidal neurons interact with three inhibitory subtypes to compute sign-specific prediction errors."
    },
    {
      "doi": "10.1371/journal.pcbi.1011921",
      "value": "SST-mediated dendritic inhibition carries top-down prediction during visuomotor mismatch",
      "method": "visuomotor prediction model",
      "metric": "SST role in predictive coding",
      "cite_key": "GalvanFraile2024",
      "condition": "visual flow mismatch",
      "study_system": "circuit model",
      "value_source_sentence": "Modeling circuit mechanisms of opposing cortical responses to visual flow perturbations."
    },
    {
      "doi": "10.1371/journal.pcbi.1013469",
      "value": "SST and PV generate distinct oscillatory patterns during predictive coding phases",
      "method": "oscillatory analysis",
      "metric": "SST role in predictive coding",
      "cite_key": "Lee2025",
      "condition": "sensory prediction",
      "study_system": "spiking network",
      "value_source_sentence": "Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding."
    }
  ],
  "comparison_id": "predictive-coding-sst-models",
  "comparison_name": "Competing predictive coding models: SST role in prediction vs error computation",
  "comparison_type": "cross-study conflict",
  "what_it_reveals": "Models disagree on whether SST neurons carry the prediction signal (top-down expectations mediated through dendritic inhibition) or contribute to computing the prediction error. This reveals a fundamental unresolved question about SST's role in cortical inference.",
  "homogeneity_check": {
    "caveats": "Different model architectures (mean-field vs spiking vs rate), different definitions of prediction error, different brain regions. The disagreement may partly reflect different levels of abstraction rather than genuine conflict.",
    "comparable": false
  },
  "suggested_plot_type": "grouped bar"
}

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  "paper:paper-7d015d30f267",
  "paper:paper-af1a62e9cd4a",
  "paper:paper-d67d30eba36a"
]

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  "notes": [
    "Local review repositories are read-only inputs.",
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