An artificial neuron is a computational unit that processes information using a mathematical function that takes in a set of inputs, performs some computation on them, and generates an output. It mimics the behavior of a biological neuron, which receives signals from other neurons through its dendrites, processes them in its cell body, and sends out an output signal through its axon to other neurons.
An artificial neuron consists of three components:
Input weights: Each input to the neuron is associated with a weight, which represents the strength of the connection between the input and the neuron.
Activation function: The activation function is a mathematical function that is applied to the weighted sum of the inputs. It determines the output of the neuron.
Bias: The bias is an additional input to the neuron that is not associated with any input value. It allows the activation function to be shifted left or right, enabling the neuron to learn different patterns in the data.
The input weights and bias are adjusted during the learning process to optimize the performance of the neural network on a given task. This is done through a process called backpropagation, where the network is trained on a set of labeled examples and the error between the predicted outputs and the true outputs is used to adjust the weights and bias.
