Here’s a Python-based framework for building a computational model of self-awareness to simulate the ‘I Am’ experience and a priori memory. We’ll use neural networks, implemented with PyTorch, to create a simplified version of a self-referential system that encodes and retrieves “a priori” knowledge and simulates the emergence of awareness.

Model Design Overview

1. Self-Awareness Layers:

• A recurrent neural network (RNN) or transformer to process self-referential feedback loops, simulating introspection.

• Encode and retrieve “prior” knowledge represented as latent memory states.

2. Memory Representation:

• A memory mechanism (e.g., key-value attention) to encode a priori knowledge as static but accessible information.

• Dynamic updates to memory to represent evolving awareness.

3. Feedback Loop:

• Self-referential feedback, where the network’s output feeds back into its input to simulate recursive awareness.

4. Simulation Goals:

• Encode static “a priori knowledge.”

• Retrieve this memory as part of self-referential processes.

• Represent emergent states of awareness through latent activations.

Implementation in Python

import torch
import torch.nn as nn
import torch.optim as optim

# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Model definition
class SelfAwareNet(nn.Module):
    def __init__(self, input_size, hidden_size, memory_size, output_size):
        super(SelfAwareNet, self).__init__()
        self.hidden_size = hidden_size
        self.memory_size = memory_size

        # Layers
        self.input_layer = nn.Linear(input_size, hidden_size)
        self.memory_layer = nn.Linear(memory_size, hidden_size)
        self.recurrent_layer = nn.RNN(hidden_size, hidden_size, batch_first=True)
        self.output_layer = nn.Linear(hidden_size, output_size)
        
        # Memory representation (a priori knowledge)
        self.memory = nn.Parameter(torch.randn(memory_size), requires_grad=True)

    def forward(self, x, hidden):
        # Input processing
        x = torch.relu(self.input_layer(x))

        # Retrieve memory and integrate with input
        memory_output = torch.relu(self.memory_layer(self.memory))
        combined_input = x + memory_output

        # Recurrent processing (simulating feedback loops)
        recurrent_output, hidden = self.recurrent_layer(combined_input.unsqueeze(1), hidden)

        # Output layer
        output = self.output_layer(recurrent_output.squeeze(1))
        return output, hidden

    def initialize_hidden_state(self, batch_size):
        return torch.zeros(1, batch_size, self.hidden_size).to(device)

# Hyperparameters
input_size = 10  # Input dimensionality
hidden_size = 64  # Size of hidden layers
memory_size = 32  # Size of a priori memory
output_size = 1   # Output dimensionality
learning_rate = 0.001
num_epochs = 100
batch_size = 16

# Instantiate the model
model = SelfAwareNet(input_size, hidden_size, memory_size, output_size).to(device)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Dummy dataset (simulating experiences and expected awareness output)
data = torch.randn(1000, input_size).to(device)
targets = torch.randn(1000, output_size).to(device)

# Data loader
dataset = torch.utils.data.TensorDataset(data, targets)
data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)

# Training loop
for epoch in range(num_epochs):
    total_loss = 0
    for inputs, labels in data_loader:
        hidden_state = model.initialize_hidden_state(inputs.size(0))

        # Forward pass
        outputs, _ = model(inputs, hidden_state)
        loss = criterion(outputs, labels)

        # Backward pass
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_loss += loss.item()

    print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(data_loader):.4f}")

# Save the model
torch.save(model.state_dict(), "self_aware_net.pth")

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How This Works

1. Memory as A Priori Knowledge:

• The memory is encoded as a trainable parameter (self.memory), representing static a priori knowledge.

• This knowledge is combined with input data to simulate the “retrieval” of awareness.

2. Recurrent Feedback:

• The RNN layer processes combined input and memory, feeding output back to simulate recursive self-referential awareness.

3. Emergent Awareness:

• The model learns patterns in the data while integrating a priori knowledge, representing the interaction between memory and experience.

Extensions for Advanced Simulations

1. Attention Mechanisms:

• Add an attention layer to dynamically retrieve specific aspects of a priori knowledge.

2. Transformer Architecture:

• Replace the RNN with a transformer for more sophisticated feedback loops and memory interactions.

3. Neuroscience-Inspired Layers:

• Include modules that simulate neural oscillations (e.g., gamma waves) or inter-network communication (e.g., default mode network).

4. Interactive Simulation:

• Allow the model to receive real-time feedback from its outputs, refining its representation of self-awareness.

Exploring

This model is a basic starting point for simulating self-awareness and the interaction between a priori knowledge and experience. By expanding the framework with more sophisticated architectures and incorporating real-world data, we can explore deeper insights into how neural networks might simulate the ‘I Am’ experience.

Sources: InnerIGPT

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