A network starts with an input, somewhat like a sensory organ. Information then flows through layers of neurons, where each neuron is connected to many other neurons. If a particular neuron receives enough stimuli, then it sends a message to any other neuron is it connected to through its axon. Similarly, an artificial neural network has an input layer of data, one or more hidden layers of classifiers, and an output layer. Each node in each hidden layer is connected to a node in the next layer. When a node receives information, it sends along some amount of it to the nodes it is connected to. The amount is determined by a mathematical function called an activation function, such as sigmoid or tanh.