A neural network is typically structured with an input layer, one or more hidden layers, and an output layer. Its design is historically inspired by biological neurons: dendrites are modeled as inputs $x_i$, synapses as synaptic weights $w_i$, and the nucleus as an aggregator that computes a weighted sum $y = \sum_i x_i w_i + b$, potentially followed by a nonlinear activation function $\sigma(y)$. The resulting signal is then transmitted to the output, analogous to a biological axon. A positive weight reflects an excitatory connection, while a negative value signifies an inhibitory connection.