Neuraxon – A New Neural Growth and Computation Blueprint

2 hours ago 1

Experience Neuraxon's trinary neural dynamics with our interactive 3D visualization at HuggingFace Spaces.

🧠 Build Custom Networks: Configure neurons, synapses, and plasticity parameters
🎯 Interactive Controls: Manually set input neuron states (excitatory/neutral/inhibitory)
🔬 Live Neuromodulation: Adjust dopamine 🎯, serotonin 😊, acetylcholine 💡, and norepinephrine ⚡ in real-time
📊 3D Visualization: Watch neural activity flow through the network with curved synaptic connections
⚙️ Preset Configurations: Try small networks, large networks, high plasticity modes, and more
▶️ Real-time Simulation: Run continuous processing and observe emergent dynamics

No installation required—just open your browser and explore!

Interactive 3D visualization showing neural activity and neuromodulator flow

Neuraxon is a bio-inspired neural network framework that extends beyond traditional perceptrons through trinary logic (-1, 0, 1), capturing excitatory, neutral, and inhibitory dynamics found in biological neurons.

Unlike conventional neural networks that use discrete time steps and binary activation, Neuraxon features:

Continuous processing where inputs flow as constant streams
Multi-timescale computation at both neuron and synapse levels
Dynamic plasticity with synaptic formation, collapse, and rare neuron death
Neuromodulation inspired by dopamine, serotonin, acetylcholine, and norepinephrine
Spontaneous activity mirroring task-irrelevant yet persistent brain processes

This implementation includes a hybridization with Qubic's Aigarth Intelligent Tissue, demonstrating evolutionary approaches to neural computation.

Check out our paper for complete theoretical foundations and biological inspirations!

Neuraxons operate in three states:

+1 (Excitatory): Active firing, promoting downstream activity
0 (Neutral): Subthreshold processing, enabling subtle modulation
-1 (Inhibitory): Active suppression of downstream activity

This third "neutral" state models:

Metabotropic receptor activation
Silent synapses that can be "unsilenced"
Subthreshold dendritic integration
Neuromodulatory influences

Each synapse maintains three dynamic weights:

w_fast # Ionotropic (AMPA-like), τ ~5ms - rapid signaling w_slow # NMDA-like, τ ~50ms - sustained integration w_meta # Metabotropic, τ ~1000ms - long-term modulation

Continuous Time Processing

Unlike discrete time-step models, Neuraxon processes information continuously:

τ (ds/dt) = -s + Σ w_i·f(s_i) + I_ext(t)

This enables:

Real-time adaptation to streaming inputs
Natural temporal pattern recognition
Biologically plausible dynamics

git clone https://github.com/DavidVivancos/Neuraxon.git cd Neuraxon pip install -r requirements.txt

from neuraxon import NeuraxonNetwork, NetworkParameters # Create network with default biologically-plausible parameters params = NetworkParameters( num_input_neurons=5, num_hidden_neurons=20, num_output_neurons=5 ) network = NeuraxonNetwork(params) # Set input pattern (trinary states: -1, 0, 1) network.set_input_states([1, -1, 0, 1, -1]) # Run continuous simulation for step in range(100): network.simulate_step() if step % 20 == 0: outputs = network.get_output_states() print(f"Step {step}: Outputs = {outputs}") # Modulate network behavior via neuromodulators network.modulate('dopamine', 0.8) # Enhance learning network.modulate('serotonin', 0.6) # Adjust plasticity # Save network state from neuraxon import save_network save_network(network, "my_network.json")

Input Layer (5 neurons) ↓ ↑ (bidirectional ring connectivity) Hidden Layer (20 neurons) ↓ ↑ (with spontaneous activity) Output Layer (5 neurons) Constraints: - Small-world connectivity (~5% connection probability) - No output → input connections - Dynamic topology via structural plasticity

Neuraxon implements continuous weight evolution inspired by STDP:

# Weights evolve based on pre/post activity and neuromodulators # LTP: pre=1, post=1 → strengthen synapse # LTD: pre=1, post=-1 → weaken synapse # Neutral state provides nuanced control

# Synapses can form, strengthen, weaken, or die # Neurons can die if health drops below threshold (hidden layer only) # Silent synapses can be "unsilenced" through correlated activity

# Four neuromodulators with distinct roles: neuromodulators = { 'dopamine': 0.1, # Learning & reward 'serotonin': 0.1, # Mood & plasticity 'acetylcholine': 0.1, # Attention & arousal 'norepinephrine': 0.1 # Alertness & stress response }

Neuraxon is particularly suited for:

Continuous learning systems that adapt in real-time
Temporal pattern recognition in streaming data
Embodied AI and robotics requiring bio-realistic control
Adaptive signal processing with non-stationary inputs
Cognitive modeling of brain-like computation
Energy-efficient AI leveraging sparse, event-driven processing

Visit our HuggingFace Space for a fully interactive 3D visualization where you can:

Configure all network parameters through an intuitive GUI
Visualize neurons color-coded by type (Blue input, pink mid, red output) and state:
- High Intesity = Excitatory (+1)
- Mid Intesity = Inhibitory (-1)
- Dark = Neutral (0)
Watch neuromodulator particles (emoji sprites) flow along synaptic pathways
Control input patterns and observe how they propagate through the network
Experiment with different neuromodulator levels and see their effects
Compare preset configurations (minimal, balanced, highly plastic, etc.)

The demo features a 3D sphere layout with curved synaptic connections and real-time particle effects representing neuromodulator dynamics.

📖 Configuration Parameters

All parameters have biologically plausible default ranges:

@dataclass class NetworkParameters: # Architecture num_input_neurons: int = 5 # [1, 100] num_hidden_neurons: int = 20 # [1, 1000] num_output_neurons: int = 5 # [1, 100] connection_probability: float = 0.05 # [0.0, 1.0] # Neuron dynamics membrane_time_constant: float = 20.0 # ms [5.0, 50.0] firing_threshold_excitatory: float = 1.0 # [0.5, 2.0] firing_threshold_inhibitory: float = -1.0 # [-2.0, -0.5] # Synaptic timescales tau_fast: float = 5.0 # ms [1.0, 10.0] tau_slow: float = 50.0 # ms [20.0, 100.0] tau_meta: float = 1000.0 # ms [500.0, 5000.0] # Plasticity learning_rate: float = 0.01 # [0.0, 0.1] stdp_window: float = 20.0 # ms [10.0, 50.0] # ... see code for complete parameter set

This implementation hybridizes Neuraxon with Aigarth Intelligent Tissue, combining:

Neuraxon: Sophisticated synaptic dynamics and continuous processing
Aigarth: Evolutionary framework with mutation and natural selection

The hybrid creates "living neural tissue" that:

Evolves structure through genetic-like mutations
Adapts weights through synaptic plasticity
Undergoes selection based on task performance
Exhibits emergent complexity and self-organization

If you use Neuraxon in your research, please cite:

@article{Vivancos-Sanchez-2025neuraxon, title={Neuraxon: A New Neural Growth \& Computation Blueprint}, author={David Vivancos and Jose Sanchez}, year={2025}, journal={ResearchGate Preprint}, institution={Artificiology Research, UNIR University, Qubic Science}, url={https://www.researchgate.net/publication/397331336_Neuraxon} }

We welcome contributions! Areas of interest include:

Novel plasticity mechanisms
Additional neuromodulator systems
Energy efficiency optimizations
New application domains
Visualization tools
Performance benchmarks

Please open an issue to discuss major changes before submitting PRs.

David Vivancos
Artificiology Research https://artificiology.com/ , Qubic https://qubic.org/ Science Advisor Email: [email protected]

Jose Sanchez
UNIR University , Qubic https://qubic.org/ Science Advisor
Email: [email protected]

MIT License. See LICENSE file for details.

⚠️ Important License Notice

Core Neuraxon: Licensed under MIT License (permissive, no restrictions)

Aigarth Hybrid Features: If you implement the Aigarth hybrid features described in our paper, you MUST comply with the Aigarth License, which includes: