mcp-diagram-server/sample_data.json at main · angrysky56/mcp-diagram-server · GitHub

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{
  "meta_matrix_architecture": {
    "input_layer": {
      "visual_input": "Raw visual data processing",
      "auditory_input": "Sound and speech processing",
      "textual_input": "Text and linguistic data"
    },
    "matrix_modules": {
      "V1_matrix": {
        "function": "Edge detection and basic visual features",
        "learning_rule": "predictive_alignment",
        "connections": ["V4_matrix", "hippocampus_matrix"]
      },
      "V4_matrix": {
        "function": "Color, form and complex visual processing",
        "learning_rule": "predictive_alignment",
        "connections": ["hippocampus_matrix", "temporal_cortex"]
      },
      "broca_matrix": {
        "function": "Language production and motor speech",
        "learning_rule": "predictive_alignment",
        "connections": ["wernicke_matrix", "motor_cortex"]
      },
      "wernicke_matrix": {
        "function": "Language comprehension and semantic processing",
        "learning_rule": "predictive_alignment",
        "connections": ["broca_matrix", "hippocampus_matrix"]
      },
      "hippocampus_matrix": {
        "function": "Memory formation and consolidation",
        "learning_rule": "predictive_alignment",
        "connections": ["all_matrices"]
      }
    },
    "learning_principles": {
      "local_learning": "Predictive Alignment for self-stabilization",
      "global_learning": "Inter-matrix prediction error minimization",
      "stability": "Chaos suppression through internal coherence"
    },
    "advantages": [
      "Robustness through modular stability",
      "Continuous learning without catastrophic forgetting",
      "Emergent generalization from global coherence",
      "Biological plausibility via connectome mapping"
    ]
  }
}