A perceptron is the computer's equivalent of a neuron--an artificial neuron. In the same way dendrites carry electrical impulses into a neuron which determine whether the neuron fires and sends a signal down it's axon to the axon terminals, weighted inputs to the perceptron feed an activation function that determines the output of the perceptron. The perceptron is the building block of the [[neural network]]. A perceptron is a linear classifier with binary output modeled by $z^n = b^n + \sum w_i x_i$ where $b$ is the bias term, $w_i$ is the weight for input $i$ and $x_i$ is the value of input $i$. $z$ is the **activity** of the perceptron. An activation function is applied to the activity $z$. Common activation functions are sigmoid, tanh, ReLu (linear after a certain point), and the step function. ## training algorithm Training a perceptron can be accomplished with either the perceptron rule or delta rule ([[gradient descent]]), both of which resolve to the same equation. $ \omega_j \gets \omega_j - \alpha(\hat y_i - y_i)X_{ij} $ where $\omega_j$ is the weight of the $j$th perceptron and $\alpha$ is the learning (or step) rate. ## history of the perceptron The [[perceptron]] was first proposed by Frank Rosenblatt in 1958 in the paper *The perceptron: a probabilistic model for information storage and organization in the brain*.  *The original perceptron concept from Rosenblatt (ref. 7); artificial neurons mimic the function of the brain, transforming inputs at the retina into responses.* [[activation function]]