Neuron Explorer
Experiment with a single artificial neuron. Adjust inputs, weights, bias, and activation function to understand the fundamental building block of neural networks.
Neuron Explorer
A neuron listens to signals from its neighbors. Each connection can amplify, dampen, or even reverse a signal. If the total is strong enough, the neuron fires and sends its own signal onward. Try adjusting the sliders to see when this neuron fires.
How strong is each incoming signal?
How much does each connection let through?
How eager is this neuron to fire?
Try These
Hover over a button to see what it does. Try "Your turn!" to test yourself!
Pedagogical Goals
- •Make the abstract concept of a neuron concrete and interactive
- •Show that neurons are just weighted sums with an activation function — not magic
- •Let students discover how changing weights and bias affects the output
- •Build intuition for why activation functions matter (without them, networks can only learn linear relationships)
How It Works
The explorer renders a single neuron with adjustable inputs (sliders), weights (sliders), bias (slider), and activation function (dropdown). As students adjust parameters, the weighted sum, pre-activation value, and final output update in real time. A visual diagram shows the flow from inputs through weights to the activation function.
How It Was Built
Built as a purely client-side React component with no backend dependencies. All computation happens in the browser. The visualization uses SVG for the neuron diagram and HTML sliders for parameter adjustment. Activation functions (ReLU, sigmoid, tanh, step) are implemented as simple JavaScript functions.