Artificial Abstraction :
html_content = """
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<title>Composite Neural Architecture</title>
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body {
font-family: Arial, sans-serif;
line-height: 1.6;
background-color: #f4f4f9;
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header {
background: #333;
color: #fff;
padding: 10px 0;
text-align: center;
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section {
margin: 20px;
padding: 20px;
background: #fff;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
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h1, h2, h3 {
color: #333;
}
.code-block {
background: #272822;
color: #f8f8f2;
padding: 10px;
border-radius: 5px;
overflow-x: auto;
}
.dot-cloud {
display: flex;
flex-wrap: wrap;
justify-content: center;
gap: 10px;
margin-top: 20px;
}
.dot {
width: 10px;
height: 10px;
border-radius: 50%;
background: #0077ff;
}
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<header>
<h1>Composite Neural Architecture: Hybrid Dot Cloud</h1>
</header>
<section>
<h2>1. Introduction</h2>
<p>This framework integrates diverse sensory and cognitive processes, such as vision, hearing, speech, and calculation, into a unified neural network structure called the <strong>Neural Iterative Architecture (NIA)</strong>.</p>
</section>
<section>
<h2>2. Key Components</h2>
<h3>2.1 Neural Iterative Architecture (NIA)</h3>
<p>A multi-layered architecture with iterative refinement and feedback loops, ensuring dynamic adaptability.</p>
<h3>2.2 Neuralion</h3>
<p>An interlinked network combining specialized sub-networks for each modality. Connections are mediated through hyperpoints for cross-sensory integration.</p>
<h3>2.3 Hyperpoints</h3>
<p>Central nodes representing shared states in a multidimensional semantic space.</p>
</section>
<section>
<h2>3. Workflow</h2>
<ol>
<li>Data streams enter specialized sub-networks for processing.</li>
<li>Feature vectors are transformed into hyperpoints in a composite dot cloud.</li>
<li>Hyperpoints are iteratively refined through feedback loops.</li>
<li>Refined states are broadcast globally for decision-making.</li>
</ol>
</section>
<section>
<h2>4. Visualization</h2>
<div class="dot-cloud">
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<p>Each dot represents a hyperpoint, with colors indicating modality (e.g., red for vision, green for hearing).</p>
</section>
<section>
<h2>5. Applications</h2>
<ul>
<li><strong>Autonomous Systems:</strong> Robots capable of real-time decision-making based on multimodal input.</li>
<li><strong>Creative AI:</strong> Generating novel art and music by combining sensory and cognitive data.</li>
<li><strong>Healthcare:</strong> Integrative diagnostic tools blending patient history, imaging, and real-time monitoring.</li>
</ul>
</section>
<footer>
<p style="text-align:center;">© 2025 Composite Neural Architecture Framework</p>
</footer>
</body>
</html>
"""
with open('composite_neural_architecture.html', 'w') as file:
file.write(html_content)
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import numpy as np import matplotlib.pyplot as plt import networkx as nx
class Neuron: def init(self, a, b, c): self.a = a self.b = b self.c = c
def third_order_equation(self, V, dV, ddV, I):
return ddV + self.a * dV + self.b * V + self.c - I
class CompositePointCloud: def init(self): self.points = []
def add_point(self, x, y, z, attributes):
self.points.append((x, y, z, attributes))
def visualize(self):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xs, ys, zs = zip(*[(p[0], p[1], p[2]) for p in self.points])
ax.scatter(xs, ys, zs)
plt.show()
class RandomHybridTree: def init(self): self.tree = nx.DiGraph()
def add_node(self, node):
self.tree.add_node(node)
def add_edge(self, node1, node2, weight):
self.tree.add_edge(node1, node2, weight=weight)
def visualize(self):
pos = nx.spring_layout(self.tree)
nx.draw(self.tree, pos, with_labels=True, node_size=700, node_color='skyblue', font_size=10, font_weight='bold')
labels = nx.get_edge_attributes(self.tree, 'weight')
nx.draw_networkx_edge_labels(self.tree, pos, edge_labels=labels)
plt.show()
neuron = Neuron(a=1.0, b=0.5, c=-0.1) V, dV, ddV, I = 1.0, 0.5, 0.2, 0.8 neuron_output = neuron.third_order_equation(V, dV, ddV, I) print(f"Neuron output (third-order equation): {neuron_output}")
point_cloud = CompositePointCloud() point_cloud.add_point(0.1, 0.2, 0.3, {"firing_rate": 0.5}) point_cloud.add_point(0.4, 0.5, 0.6, {"firing_rate": 0.8}) point_cloud.visualize()
hybrid_tree = RandomHybridTree() hybrid_tree.add_node("A") hybrid_tree.add_node("B") hybrid_tree.add_edge("A", "B", 0.6) hybrid_tree.visualize()
class CompositeNeuralArchitecture: def init(self): self.neurons = [] self.point_cloud = CompositePointCloud() self.hybrid_tree = RandomHybridTree()
def add_neuron(self, neuron):
self.neurons.append(neuron)
def add_point_to_cloud(self, x, y, z, attributes):
self.point_cloud.add_point(x, y, z, attributes)
def add_edge_to_tree(self, node1, node2, weight):
self.hybrid_tree.add_edge(node1, node2, weight)
def visualize_architecture(self):
print("Visualizing Composite Point Cloud...")
self.point_cloud.visualize()
print("Visualizing Random Hybrid Tree...")
self.hybrid_tree.visualize()
architecture = CompositeNeuralArchitecture() architecture.add_neuron(neuron) architecture.add_point_to_cloud(0.7, 0.8, 0.9, {"firing_rate": 0.9}) architecture.add_edge_to_tree("B", "C", 0.7) architecture.visualize_architecture() class CompositeNeuralArchitecture: def init(self): self.neurons = [] self.point_cloud = CompositePointCloud() self.hybrid_tree = RandomHybridTree()
def add_neuron(self, neuron):
self.neurons.append(neuron)
def add_point_to_cloud(self, x, y, z, attributes):
self.point_cloud.add_point(x, y, z, attributes)
def add_edge_to_tree(self, node1, node2, weight):
self.hybrid_tree.add_edge(node1, node2, weight)
def visualize_architecture(self):
print("Visualizing Composite Point Cloud...")
self.point_cloud.visualize()
print("Visualizing Random Hybrid Tree...")
self.hybrid_tree.visualize()
architecture = CompositeNeuralArchitecture() architecture.add_neuron(neuron) architecture.add_point_to_cloud(0.7, 0.8, 0.9, {"firing_rate": 0.9}) architecture.add_edge_to_tree("B", "C", 0.7) architecture.visualize_architecture()
import numpy as np import matplotlib.pyplot as plt import networkx as nx
class Neuron: def init(self, a, b, c): self.a = a self.b = b self.c = c
def third_order_equation(self, V, dV, ddV, I):
return ddV + self.a * dV + self.b * V + self.c - I
class CompositePointCloud: def init(self): self.points = []
def add_point(self, x, y, z, attributes):
self.points.append((x, y, z, attributes))
def visualize(self):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
xs, ys, zs = zip(*[(p[0], p[1], p[2]) for p in self.points])
ax.scatter(xs, ys, zs)
plt.show()
class RandomHybridTree: def init(self): self.tree = nx.DiGraph()
def add_node(self, node):
self.tree.add_node(node)
def add_edge(self, node1, node2, weight):
self.tree.add_edge(node1, node2, weight=weight)
def visualize(self):
pos = nx.spring_layout(self.tree)
nx.draw(self.tree, pos, with_labels=True, node_size=700, node_color='skyblue', font_size=10, font_weight='bold')
labels = nx.get_edge_attributes(self.tree, 'weight')
nx.draw_networkx_edge_labels(self.tree, pos, edge_labels=labels)
plt.show()
neuron = Neuron(a=1.0, b=0.5, c=-0.1) V, dV, ddV, I = 1.0, 0.5, 0.2, 0.8 neuron_output = neuron.third_order_equation(V, dV, ddV, I) print(f"Neuron output (third-order equation): {neuron_output}")
point_cloud = CompositePointCloud() point_cloud.add_point(0.1, 0.2, 0.3, {"firing_rate": 0.5}) point_cloud.add_point(0.4, 0.5, 0.6, {"firing_rate": 0.8}) point_cloud.visualize()
hybrid_tree = RandomHybridTree() hybrid_tree.add_node("A") hybrid_tree.add_node("B") hybrid_tree.add_edge("A", "B", 0.6) hybrid_tree.visualize() html_content = """
<title>Composite Neural Architecture</title> <style> body { font-family: Arial, sans-serif; line-height: 1.6; background-color: #f4f4f9; margin: 0; padding: 0; } header { background: #333; color: #fff; padding: 10px 0; text-align: center; } section { margin: 20px; padding: 20px; background: #fff; border-radius: 8px; box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); } h1, h2, h3 { color: #333; } .code-block { background: #272822; color: #f8f8f2; padding: 10px; border-radius: 5px; overflow-x: auto; } .dot-cloud { display: flex; flex-wrap: wrap; justify-content: center; gap: 10px; margin-top: 20px; } .dot { width: 10px; height: 10px; border-radius: 50%; background: #0077ff; } </style>This framework integrates diverse sensory and cognitive processes, such as vision, hearing, speech, and calculation, into a unified neural network structure called the Neural Iterative Architecture (NIA).
A multi-layered architecture with iterative refinement and feedback loops, ensuring dynamic adaptability.
An interlinked network combining specialized sub-networks for each modality. Connections are mediated through hyperpoints for cross-sensory integration.
Central nodes representing shared states in a multidimensional semantic space.
- Data streams enter specialized sub-networks for processing.
- Feature vectors are transformed into hyperpoints in a composite dot cloud.
- Hyperpoints are iteratively refined through feedback loops.
- Refined states are broadcast globally for decision-making.
Each dot represents a hyperpoint, with colors indicating modality (e.g., red for vision, green for hearing).
- Autonomous Systems: Robots capable of real-time decision-making based on multimodal input.
- Creative AI: Generating novel art and music by combining sensory and cognitive data.
- Healthcare: Integrative diagnostic tools blending patient history, imaging, and real-time monitoring.
© 2025 Composite Neural Architecture Framework
"""with open('composite_neural_architecture.html', 'w') as file: file.write(html_content)
print("HTML content has been written to 'composite_neural_architecture.html'")