🧠 Neuraxon Network Builder
By David Vivancos & Jose Sanchez - Qubic Science
Build Your Bio-Inspired Neural Network based on Neuraxon paper & Qubic's Aigarth Intelligent Tissue
Configure all parameters using the sliders below, then click "Build Network" to create and visualize your custom Neuraxon network with trinary states, continuous processing, and complex synaptic dynamics.
Configure all parameters using the sliders below, then click "Build Network" to create and visualize your custom Neuraxon network with trinary states, continuous processing, and complex synaptic dynamics.
📁 Network Identity & Management
Name used for saving/loading parameters.
🏗️ Network Architecture
Number of input neurons (1-5)
Number of hidden neurons (1-100)
Number of output neurons (1-5)
Probability of synapse formation between neurons (0.0-0.20)
⚡ Neuron Parameters
Time constant for membrane potential decay (5.0-50.0)
Firing threshold for excitatory state (+1) (0.5-2.0)
Firing threshold for inhibitory state (-1) (-2.0 to -0.5)
Rate of neuronal adaptation (0.0-0.2)
Probability of spontaneous activity (0.0-0.1)
Rate of neuron health decay without activity (0.0-0.01)
🔗 Synapse Parameters
Fast Ionotropic (AMPA-like)
Slow Ionotropic (NMDA-like)
Metabotropic
🌱 Plasticity Parameters
Synaptic plasticity learning rate (0.0-0.1)
Spike-timing-dependent plasticity window (10.0-50.0)
Minimum integrity for synapse survival (0.0-0.5)
Probability of new synapse formation (0.0-0.2)
Probability of synapse removal (0.0-0.1)
Health threshold for neuron death (0.0-0.3)
🧪 Neuromodulator Parameters
Rate of neuromodulator decay (0.0-0.5)
⏱️ Simulation Parameters
Simulation time step (0.1-10.0)
Number of initial simulation steps (1-10000)
🧠 Neuraxon Controls
Input Neurons
Simulation
Neuromodulators
Legend
Input (Box)
Hidden (Sphere)
Output (Cone)
Excitatory Synapse
Inhibitory Synapse
🎯😊💡⚡
Neuromodulators (moving along synapses)
Building Neuraxon Network...