Update node.py

9d2804b verified almost 2 years ago

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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(1))  # Changed from input_size to 1
        self.bias = nn.Parameter(torch.zeros(1))
        
        # Memory system - adjusted context size
        self.memory = CognitiveMemory(context_size=1)  # Changed from input_size to 1
        
        # Neurotransmitter levels
        self.dopamine = nn.Parameter(torch.tensor(0.5))
        self.serotonin = nn.Parameter(torch.tensor(0.5))
        
        # Store recent activations
        self.recent_activations = {}
        
    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        # Ensure inputs is a single value tensor
        inputs = inputs.reshape(1)
        
        # 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())
        
        # Store recent activation
        self.recent_activations[len(self.recent_activations)] = modulated.item()
        if len(self.recent_activations) > 100:
            self.recent_activations.pop(min(self.recent_activations.keys()))
        
        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)

	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(1)) # Changed from input_size to 1
	self.bias = nn.Parameter(torch.zeros(1))

	# Memory system - adjusted context size
	self.memory = CognitiveMemory(context_size=1) # Changed from input_size to 1

	# Neurotransmitter levels
	self.dopamine = nn.Parameter(torch.tensor(0.5))
	self.serotonin = nn.Parameter(torch.tensor(0.5))

	# Store recent activations
	self.recent_activations = {}

	def forward(self, inputs: torch.Tensor) -> torch.Tensor:
	# Ensure inputs is a single value tensor
	inputs = inputs.reshape(1)

	# 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())

	# Store recent activation
	self.recent_activations[len(self.recent_activations)] = modulated.item()
	if len(self.recent_activations) > 100:
	self.recent_activations.pop(min(self.recent_activations.keys()))

	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)