Mechanisms of surround suppression: SSN emergent vs SST-specific lateral connec…

Two fundamentally different modeling approaches explain surround suppression — one through emergent network properties (SSN) and another through explicit SST lateral connectivity. Recent data on feature-tuned SST inputs adds a new constraint that challenges both frameworks.

{
  "papers": [
    {
      "doi": "10.1016/j.neuron.2014.12.026",
      "value": "Emergent from SSN dynamics — no SST-specific connectivity required",
      "method": "power-law nonlinearity",
      "metric": "Surround suppression mechanism",
      "cite_key": "Rubin2015",
      "condition": "V1 size tuning",
      "study_system": "rate model SSN",
      "value_source_sentence": "The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex."
    },
    {
      "doi": "10.3389/fncir.2015.00060",
      "value": "Long-range lateral excitation of Martinotti cells required",
      "method": "lateral Martinotti recruitment",
      "metric": "Surround suppression mechanism",
      "cite_key": "Krishnamurthy2015",
      "condition": "V1 surround",
      "study_system": "attractor network",
      "value_source_sentence": "Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network."
    },
    {
      "doi": "10.1101/2021.03.31.437953",
      "value": "SSN with PC→VIP⊣SST⊣PC and PC→SST⊣PC loops switching with contrast",
      "method": "4-population SSN + optogenetics",
      "metric": "Surround suppression mechanism",
      "cite_key": "Mossing2021",
      "condition": "mouse V1 L2/3",
      "study_system": "SSN constrained by data",
      "value_source_sentence": "Recurrent amplification drives a transition from a positive PC→VIP⊣SST⊣PC feedback loop at small size and low contrast to a negative PC→SST⊣PC feedback loop at large size and high contrast."
    },
    {
      "doi": "10.1016/j.neuron.2025.12.021",
      "value": "Feature-tuned synaptic inputs to SST drive context-dependent suppression",
      "method": "feature-specific SST inputs",
      "metric": "Surround suppression mechanism",
      "cite_key": "Hendricks2026",
      "condition": "context-dependent suppression",
      "study_system": "experiment + model",
      "value_source_sentence": "Feature-tuned synaptic inputs to somatostatin interneurons drive context-dependent surround suppression."
    }
  ],
  "comparison_id": "surround-suppression-model-comparison",
  "comparison_name": "Mechanisms of surround suppression: SSN emergent vs SST-specific lateral connections",
  "comparison_type": "cross-study conflict",
  "what_it_reveals": "Two fundamentally different modeling approaches explain surround suppression — one through emergent network properties (SSN) and another through explicit SST lateral connectivity. Recent data on feature-tuned SST inputs adds a new constraint that challenges both frameworks.",
  "homogeneity_check": {
    "caveats": "All studying surround suppression in visual cortex but at different levels of detail and with different experimental constraints. The SSN and Martinotti models make different predictions about SST connectivity requirements.",
    "comparable": true
  },
  "suggested_plot_type": "forest plot"
}

[
  "paper:paper-6922f4d24e18",
  "paper:paper-6f3fdb602713",
  "paper:paper-7cf52a12f6e4",
  "paper:paper-e15053dfa039"
]

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  "notes": [
    "Local review repositories are read-only inputs.",
    "SciDEX stores paper metadata, structured evidence, file pointers, and short citation contexts; it does not copy full review prose."
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  "source_commit_sha": "89b7e9787cd90e942b0adb531d549af3ddad30f1",
  "source_repository_url": "https://github.com/AllenNeuralDynamics/ComputationalReviewSST"
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