Quick Start
▼Choose a preset to instantly load a network architecture
Dataset
▼Select the data pattern the network will learn to classify
Data Pattern
The 2D pattern shown in the right panel
Noise
10%
Higher noise = harder classification task
Neuron Model
▼Configure how neurons compute and communicate
Architecture
Each model processes signals differently
Topology
How layers connect to each other
Activation
Non-linear function applied at each neuron
Layers
▼Define the network depth and width
Architecture
More layers = deeper network = more complex patterns
Simulation
▼Control the neural dynamics and learning parameters
Spike Rate
45%
How often neurons fire. Above 85% triggers cascade!
Density
60%
Percentage of possible connections that exist
Time Scale
0.50x
Speed of the simulation (0.05x to 2x)
Threshold
0.75
Voltage needed for a neuron to fire a spike
Learning Rate
0.030
How fast weights update during training
Visual Style
▼Node Size
0.30
Layer Spacing
10
Node Spacing
1.8
Edges
Labels
Glow
Grid
Weight Colors
STDP Learning
Active Color
Neuron glow
Spike Color
Signal pulse
0 neurons
0 edges
0 spk/s
60 fps
NEURAL CASCADE
Membrane Oscilloscope
STDP Learning Rule
Input Data
0
Neurons
0
Synapses
0
Firing
0
Avg mV
Spike Raster
Membrane Potential
Output Activations