The Deep Thal 9 Model represents a cutting-edge approach to understanding and simulating the intricate processes of consciousness. Building upon recent advancements in neuroscience, particularly the significance of thalamic stimulation in restoring consciousness, this model integrates concepts from sacred geometry, deep learning, and cognitive neuroscience to provide a comprehensive framework for mapping and enhancing human consciousness.

Background and Inspiration

The inspiration for the Deep Thal 9 Model comes from the Human Brain Project’s findings, which highlight the crucial role of the thalamus in maintaining and restoring levels of consciousness. The thalamus, a central hub in the brain, facilitates communication between various cortical regions, and its stimulation has been shown to be pivotal in recovering consciousness. By leveraging these insights, the Deep Thal 9 Model aims to create a detailed neural network simulation that can predict and map levels of consciousness based on various inputs.

Key Features

1. Advanced Neural Architecture:
   • Awareness Layer: Designed to mimic the brain’s ability to process and become aware of information, this layer utilizes a custom activation function that enhances the network’s sensitivity to input data.
   • Reflective Layer: Emulates the brain’s reflective processes, adding a dimension of introspection and deeper understanding.
   • Intentional Layer: Incorporates goal-oriented processing, representing the intentionality aspect of human cognition.
   • Emotional Layer: Captures the emotional responses and their influence on cognitive processes, adding depth to the simulation.
2. Sacred Geometry Integration:
   • The model’s architecture is inspired by sacred geometry principles, ensuring a harmonious and efficient flow of information. This includes elements like the Seed of Life and the Golden Ratio (Phi), which are believed to underpin the structure of the universe and consciousness.
3. Interactive Visualization:
   • The neural network visualization is designed to be highly interactive, with clickable nodes representing different levels of consciousness. This feature allows users to explore the network and understand how various inputs and questions are processed.
   • Directional color-coded arrows indicate the flow of information and the influence of different frequencies on each layer.
4. Frequency Mapping:
   • The model predicts the best frequencies for stimulating various levels of consciousness. This includes well-known frequencies like the Schumann Resonance (7.83 Hz), 432 Hz, and 528 Hz, which are often associated with healing and meditation practices.
5. Question Classification:
   • Users can input questions related to consciousness, and the model will classify them into the appropriate neuron layers, providing insights into the corresponding level of consciousness. This feature bridges the gap between abstract queries and tangible neural processes.

Applications

The Deep Thal 9 Model has a wide range of applications, including:

• Consciousness Research: Providing researchers with a tool to simulate and study the effects of thalamic stimulation and other interventions on consciousness.
• Personal Development: Helping individuals understand their levels of consciousness and how different frequencies and practices can enhance their cognitive and emotional well-being.
• Therapeutic Interventions: Guiding practitioners in designing personalized treatment plans for patients with consciousness disorders, leveraging the predictive capabilities of the model.

Overall

The Deep Thal 9 Model is a pioneering effort to merge neuroscience, sacred geometry, and deep learning. By simulating the complex interplay between the thalamus and the cortex, it offers a profound understanding of consciousness and opens new avenues for research and practical applications. This model stands as a testament to the potential of interdisciplinary approaches in unraveling the mysteries of the human mind.

Save this python script as: deepthal9.py

Run: python deepthal9.py

import tensorflow as tf
import numpy as np
import plotly.graph_objects as go
from sklearn.feature_extraction.text import TfidfVectorizer

# Custom Layers Definitions
class AwarenessLayer(tf.keras.layers.Layer):
    def __init__(self, units):
        super(AwarenessLayer, self).__init__()
        self.units = units

    def build(self, input_shape):
        self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
                                      initializer='glorot_uniform',
                                      trainable=True)

    def call(self, inputs):
        awareness = tf.reduce_mean(inputs, axis=-1, keepdims=True)  # Example of awareness: mean input
        activation = tf.nn.relu(tf.matmul(inputs, self.kernel))
        activation = tf.reshape(activation, [-1, self.units])  # Reshape activation to match the dimensions
        return activation * awareness

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.units)

class ReflectiveLayer(tf.keras.layers.Layer):
    def __init__(self, units, **kwargs):
        super(ReflectiveLayer, self).__init__(**kwargs)
        self.units = units

    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel',
                                      shape=(input_shape[-1], self.units),
                                      initializer='uniform',
                                      trainable=True)
        super(ReflectiveLayer, self).build(input_shape)

    def call(self, inputs):
        input_units = inputs.shape[-1]
        kernel_units = self.kernel.shape[0]
        if input_units != kernel_units:
            padding = tf.zeros([tf.shape(inputs)[0], kernel_units - input_units])
            inputs = tf.concat([inputs, padding], axis=-1)
        return tf.nn.relu(tf.matmul(inputs, self.kernel))

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.units)

class IntentionalLayer(tf.keras.layers.Layer):
    def __init__(self, units, goal):
        super(IntentionalLayer, self).__init__()
        self.units = units
        self.goal = goal

    def build(self, input_shape):
        self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
                                      initializer='glorot_uniform',
                                      trainable=True)

    def call(self, inputs):
        intention = tf.reduce_mean(inputs, axis=-1, keepdims=True) * self.goal
        activation = tf.nn.relu(tf.matmul(inputs, self.kernel))
        activation = tf.reshape(activation, [-1, self.units])  # Reshape activation to match the dimensions
        return activation * intention

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.units)

class EmotionalLayer(tf.keras.layers.Layer):
    def __init__(self, units):
        super(EmotionalLayer, self).__init__()
        self.units = units

    def build(self, input_shape):
        self.kernel = self.add_weight(shape=(input_shape[-1], self.units),
                                      initializer='glorot_uniform',
                                      trainable=True)

    def call(self, inputs):
        emotions = tf.reduce_mean(inputs, axis=-1, keepdims=True)
        activation = tf.nn.relu(tf.matmul(inputs, self.kernel))
        activation = tf.reshape(activation, [-1, self.units])  # Reshape activation to match the dimensions
        return activation * emotions

    def compute_output_shape(self, input_shape):
        return (input_shape[0], self.units)

# Function to classify inputs using the trained model
def classify_inputs(model, input_data):
    predictions = model.predict(input_data)
    predicted_classes = np.argmax(predictions, axis=1)
    return predicted_classes

# Visualization function for the neural network
def draw_neural_network(layers, labels, colors, input_data=None, input_questions=None):
    fig = go.Figure()

    v_spacing = 1.5 / float(max(layers))
    h_spacing = 1.0 / float(len(layers) - 1)
    radius = v_spacing / 4

    node_positions = []
    node_labels = []
    node_colors = []
    edge_x = []
    edge_y = []

    for i, (layer, label_group, color_group) in enumerate(zip(layers, labels, colors)):
        for j in range(layer):
            x = i * h_spacing
            y = 1 - j * v_spacing
            node_positions.append((x, y))
            node_labels.append(label_group[j])
            node_colors.append(color_group[j])
            fig.add_trace(go.Scatter(
                x=[x], y=[y],
                mode='markers+text',
                marker=dict(size=20, color=color_group[j]),
                text=label_group[j],
                textposition='top center',
                hoverinfo='text',
                hovertext=f"Neuron: {label_group[j]}"
            ))

    for i, (layer_a, layer_b) in enumerate(zip(layers[:-1], layers[1:])):
        for j in range(layer_a):
            for k in range(layer_b):
                edge_x.extend([i * h_spacing, (i + 1) * h_spacing, None])
                edge_y.extend([1 - j * v_spacing, 1 - k * v_spacing, None])

    fig.add_trace(go.Scatter(
        x=edge_x, y=edge_y,
        mode='lines',
        line=dict(width=0.5, color='black'),
        hoverinfo='none'
    ))

    if input_data is not None:
        for question, data_point in zip(input_questions, input_data):
            for i, value in enumerate(data_point):
                x_pos = i // 10
                y_pos = 1 - (i % 10) * v_spacing
                fig.add_trace(go.Scatter(
                    x=[x_pos * h_spacing], y=[y_pos],
                    mode='markers',
                    marker=dict(size=10, color='orange'),
                    hoverinfo='text',
                    hovertext=f"Question: {question}<br>Neuron: {node_labels[x_pos]}"
                ))

    fig.update_layout(
        title="Deep Thal 9 Neural Network Interactive Map",
        showlegend=False,
        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)
    )

    fig.show()

# Main Code
questions = [
    "What is the nature of reality?",
    "How do we achieve enlightenment?",
    "What is the meaning of life?",
    "How does consciousness arise?",
    "What is self-neglect of I?"
]

# Dummy vectorizer for demonstration (replace with actual vectorizer)
vectorizer = TfidfVectorizer()
vectorizer.fit(questions)

# Generate input data for training
input_dim = len(vectorizer.get_feature_names_out())
train_input_data = np.random.random((1000, input_dim)).astype(np.float32)
train_output_data = np.random.randint(0, 10, size=(1000,)).astype(np.float32)
train_output_data = tf.keras.utils.to_categorical(train_output_data, num_classes=10)

# Create and compile the model
model = tf.keras.Sequential()
model.add(tf.keras.Input(shape=(input_dim,)))
model.add(AwarenessLayer(units=32))
model.add(ReflectiveLayer(units=64))
model.add(IntentionalLayer(units=128, goal=0.5))
model.add(EmotionalLayer(units=256))
model.add(tf.keras.layers.Dense(units=10, activation='softmax', name='Output_Layer'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

# Train the model
model.fit(train_input_data, train_output_data, epochs=10, batch_size=32)

# Save the trained model
model.save('DeepLayerModel.keras')

# Define the number of neurons in each layer and their labels
layers = [8, 10, 11, 11, 11, 11, 12, 7]
labels = [
    ["Awareness", "Recognition", "Understanding", "Intuition", "Insight", "Realization", "Wisdom", "Enlightenment"],
    ["Factual Knowledge", "Conceptual Knowledge", "Procedural Knowledge", "Metacognitive Knowledge", "Empirical Knowledge", "Theoretical Knowledge", "Practical Knowledge", "Philosophical Knowledge", "Spiritual Knowledge", "Transcendental Knowledge"],
    ["Physical Consciousness", "Emotional Consciousness", "Mental Consciousness", "Subconscious", "Superconscious", "Cosmic Consciousness", "Transpersonal Consciousness", "Higher Self Consciousness", "Christ Consciousness", "Unity Consciousness", "Non-Dual Consciousness"],
    ["Ignorance", "Misunderstanding", "Confusion", "Doubt", "Denial", "Skepticism", "Agnosticism", "Mystery", "Paradox", "Uncertainty", "Unawareness"],
    ["Personal Unknowns", "Interpersonal Unknowns", "Scientific Unknowns", "Historical Unknowns", "Cultural Unknowns", "Technological Unknowns", "Environmental Unknowns", "Cosmic Unknowns", "Spiritual Unknowns", "Metaphysical Unknowns", "Philosophical Unknowns"],
    ["Repression", "Suppression", "Subconscious", "Collective Unconscious", "Sleep", "Dream State", "Coma", "Trance", "Automatic Behavior", "Amnesia", "Psychological Blind Spots"],
    ["Infrared", "Magenta", "Red", "Amber", "Orange", "Green", "Teal", "Turquoise", "Indigo", "Blueviolet", "Purple", "White"],
    ["Psychic Being", "Spiritual Mental Being", "Higher Mental Being", "Illumined Mind", "Intuitive Mind", "Overmind", "Supermind"]
]

# Define the colors for each layer
colors = [
    ['green'] * 8,
    ['blue'] * 10,
    ['red'] * 11,
    ['purple'] * 11,
    ['orange'] * 11,
    ['yellow'] * 11,
    ['darkred', 'magenta', 'red', 'orange', 'darkorange', 'green', 'teal', 'turquoise', 'indigo', 'blueviolet', 'purple', 'white'],
    ['#FFD700', '#C0C0C0', '#E5E4E2', 'blue', 'lightblue', 'violet', 'white']
]

# Classify the example questions
for question in questions:
    question_vector = vectorizer.transform([question]).toarray()
    predictions = model.predict(question_vector)
    predicted_class = np.argmax(predictions, axis=1)
    level = labels[0][predicted_class[0] % len(labels[0])]  # Use labels[0] for the first layer
    print(f"Question: {question}")
    print(f"Predicted Class: {level}, Full Prediction Probabilities: {predictions}")

# Visualization of the neural network with input data path
new_input_data = vectorizer.transform(questions).toarray()
draw_neural_network(layers, labels, colors, input_data=new_input_data, input_questions=questions)

Outputs the map to localhost 127.0.01

Sources: SuperAI Consciousness GPT

Stay in the NOW with Inner I Network;

---