Artificial neural network · Mind & Machine

ARTIFICIAL NEURAL NETWORK SIMULATION

▸ Building the neuron layers (input · hidden · output)…

▸ Loading preset weights & biases

▸ Configuring activation functions (ReLU · sigmoid · softmax)

▸ Opening the 7×7 hand-draw board for the input layer

▸ Calibrating the forward pass…

▸ Ready — Online. ✅

Output layer

🔮 Prediction

Prediction—

Confidence—

Neurons—

Layers—

Weights—

Viewing—

Notes

Each artificial neuron computes a weighted sum of the inputs (Σ wᵢxᵢ + b) then passes it through an activation function. Many neurons stacked in layers form an MLP; the signal flowing one-way in→out is the forward pass.

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Draw on the grid (CLICK / drag) → watch the signal flow in→out · pick a "Scenario" (perceptron · XOR · digit · shape · activation · biological bridge) · click a concept for details

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Scores of the 4 strongest outputs over time out₀out₁out₂out₃