Neurons
Neurons are the fundamental units of computation in an artificial neural network (ANN). They are inspired by the structure and function of biological neurons in the human brain. Neurons receive inputs, perform computations, and generate outputs, which collectively enable the network to learn and make predictions. Here are the key components and characteristics of neurons in an ANN:
1. Inputs:
Neurons receive inputs from other neurons or directly from the input data. Each input is associated with a weight, which represents the importance or strength of that connection. The weights multiply the inputs and determine their contribution to the neuron's overall computation.
2. Weighted Sum:
The weighted sum is computed by multiplying each input by its corresponding weight and summing up the results. Mathematically, it can be expressed as:
weighted_sum = Σ(input_i * weight_i)
The weighted sum represents the total input to the neuron.
3. Activation Function:
After computing the weighted sum, an activation function is applied to introduce non-linearities and determine the output of the neuron. The activation function takes the weighted sum as input and produces the neuron's output or activation.
Commonly used activation functions include sigmoid, tanh, ReLU, softmax, and others. The choice of activation function depends on the problem being solved and the desired characteristics of the network.
4. Bias:
In addition to inputs and weights, neurons often have an associated bias term. A bias is an additional constant value that is added to the weighted sum before applying the activation function. The bias allows for fine-tuning and helps shift the activation level of the neuron.
5. Output:
The output of a neuron is the result of applying the activation function to the weighted sum plus the bias. It represents the activation level or firing rate of the neuron and is passed on as input to other neurons in subsequent layers.
Neurons in an ANN are organized in layers, with each layer typically consisting of multiple neurons. The input layer receives the input data, and subsequent hidden layers perform computations and extract relevant features. The output layer produces the final predictions or outputs based on the computations performed in the hidden layers.
Through the training process, the weights and biases of the neurons are iteratively adjusted to minimize the error and improve the network's performance. By learning the appropriate weights, the network can capture complex patterns and relationships in the data, enabling it to make accurate predictions or classifications.
Overall, neurons are the building blocks of ANNs and their computations, along with the connections between them, allow the network to learn and perform complex tasks.
