Single neuron model diagram illustrating the fundamental building block of neural networks with input vector X(t), weight matrix Wx, bias term b, and sigmoid activation function. Data flows from multiple inputs x1(t) through xn(t) into a summation node that computes z = Wx*X(t) + b, then passes through the activation function sigma(z) to produce output y(t). This architecture demonstrates the core mathematical operations—weighted sum, bias addition, and nonlinear activation—essential for understanding how neurons learn and transform data. Fork this diagram to customize activation functions, add batch normalization, or extend to multi-layer networks for your documentation or educational materials.