TRON: Symbolic Neural Model Framework

TRON is a modular symbolic cognition engine written in Python.
It provides a flexible foundation for building evolving neural fields using pulse-based activation, dynamic clusters, and memory structures.

Inspired by the modularity and plasticity of the human brain, TRON treats intelligence as a field of interconnected, autonomous nodes. Each node maintains identity, activity, and symbolic lineage.

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Key Concepts

• NodeField: A dynamic graph of symbolic nodes and connections.
• Nodes: The smallest cognitive unit; stores state, receives pulses, emits signals.
• NodeRoot: Weighted directional connection between nodes.
• Pulse: Unit of signal propagation (analog, binary, or symbolic).
• Clusters: Modular logic, input, memory, and output structures built from nodes.
• Scheduler: Tick-based time controller for pulse propagation and learning.
• Plasticity: All connections support drift, adaptation, and runtime reconfiguration.

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Install

Install directly from GitHub:

pip install git+https://github.com/IMisbahK/tron.git

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Example

from tron.engine import Node, NodeRoot, NodeField, Pulse

nodeA = Node()
nodeB = Node()

field = NodeField()
field.addNode(nodeA)
field.addNode(nodeB)

connection = NodeRoot(source=nodeA, target=nodeB, weight=0.8)
field.connectNodes(connection)

pulse = Pulse(strength=1.0)
nodeA.emitPulse(pulse)

field.tick()  

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Project Structure

tron/
├── engine/        # Core: nodes, roots, pulse, field logic
├── clusters/      # Prebuilt cluster types (logic, memory, etc.)
├── ops/           # Training, mutation, merging, awakening
├── symbols/       # Symbolic tagging, rationale
├── io/            # Serialization and loading
├── api/           # Public Python interface
├── tests/         # Unit tests
├── docs/          # Developer + model architecture docs
└── examples/      # Sample projects and guided builds
├── models/        # Sample projects and guided builds

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Documentation

Full documentation is located in the docs/ folder and will soon be available online.

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License

MIT License

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Author

Developed by Misbah Khursheed
https://github.com/IMisbahK
Contact: m.misbahkhursheed@gmail.com