Create node.py

node.py

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+import torch
+import torch.nn as nn
+from .memory import CognitiveMemory
+
+class CognitiveNode(nn.Module):
+    """Differentiable cognitive node with dynamic plasticity"""
+    def __init__(self, node_id: int, input_size: int):
+        super().__init__()
+        self.id = node_id
+        self.input_size = input_size
+        self.activation = 0.0
+        
+        # Dynamic input weights with Hebbian plasticity
+        self.weights = nn.Parameter(torch.randn(input_size) * 0.1)
+        self.bias = nn.Parameter(torch.zeros(1))
+        
+        # Memory system
+        self.memory = CognitiveMemory(context_size=input_size)
+        
+        # Neurotransmitter levels
+        self.dopamine = nn.Parameter(torch.tensor(0.5))
+        self.serotonin = nn.Parameter(torch.tensor(0.5))
+        
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        # Memory influence
+        mem_context = self.memory.retrieve(inputs)
+        
+        # Combine inputs with memory context
+        combined = inputs * 0.7 + mem_context * 0.3
+        
+        # Adaptive activation with neurotransmitter modulation
+        base_activation = torch.tanh(combined @ self.weights + self.bias)
+        modulated = base_activation * (1 + self.dopamine - self.serotonin)
+        
+        # Update memory
+        self.memory.add_memory(inputs, modulated.item())
+        
+        return modulated
+    
+    def update_plasticity(self, reward: float):
+        """Update neurotransmitter levels based on reward signal"""
+        self.dopamine.data = torch.sigmoid(self.dopamine + reward * 0.1)
+        self.serotonin.data = torch.sigmoid(self.serotonin - reward * 0.05)