# Deep Neural Networks

*Fri, 08 Mar 2024 07:44:16 GMT*
— Properties

** Properties**

Key | Value |
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Identifier | deep-neural-networks |

Name | Deep Neural Networks |

Type | Topic |

Creation timestamp | Fri, 08 Mar 2024 07:44:16 GMT |

Modification timestamp | Fri, 08 Mar 2024 08:39:11 GMT |

In the context of a neural network, a neuron, also known as a node or a perceptron, is a fundamental computational unit that processes information. Neurons are inspired by the biological neurons found in the human brain, and they play a crucial role in artificial neural networks. The basic components and functions of a neuron in a neural network include:

**Inputs**: A neuron receives input signals from one or more other neurons or external sources. Each input is associated with a weight, which represents the strength of the connection between the neurons.**Weights**: Each input signal is multiplied by a corresponding weight. These weights determine the importance of each input in influencing the neuron's output. The weights are adjustable parameters that the neural network learns during training.**Summation**: The weighted inputs are summed together to produce a weighted sum. This sum represents the total input to the neuron.**Activation Function**: The weighted sum is then passed through a nonlinear activation function. The activation function introduces non-linearity to the neuron's output. It determines whether the neuron should be activated (output a non-zero value) based on the computed sum.**Output**: The output of the neuron, often referred to as the activation or the response, is the result of the activation function applied to the weighted sum. This output is then used as the input for neurons in the next layer of the neural network.

In summary, a neuron in a neural network processes information by receiving inputs, applying weights to these inputs, summing them up, and passing the result through an activation function to produce an output. Neural networks consist of layers of interconnected neurons, forming a complex architecture that can learn and model intricate patterns in data through training.

### Firing (Neuron)

The concept of a neuron "firing" in a neural network is metaphorical and is closely related to the activation function. The activation function of a neuron determines whether the neuron should be activated (or "fire") based on the weighted sum of its inputs.

Here's a brief explanation:

**Weighted Sum of Inputs**: In a neural network, each neuron receives input signals from the neurons in the previous layer, and each input is multiplied by a corresponding weight. The weighted sum of these inputs is then calculated.**Activation Function**: The activation function is applied to the weighted sum to introduce non-linearity to the neuron's output. The activation function decides whether the neuron should be activated (output a non-zero value) or not (output zero) based on the result of the weighted sum.- If the result of the weighted sum, often referred to as the "activation," exceeds a certain threshold, the neuron is activated.
- If the activation is below the threshold, the neuron remains inactive.
**Metaphor of Firing**: The term "firing" is a metaphorical description of the neuron's activation state. When a neuron "fires," it means that its output is non-zero or exceeds a certain threshold, indicating that it has responded to the input and transmitted a signal to the next layer in the neural network.

**Topic location**

- Map: Artificial Intelligence — Info
- Topic: Deep Neural Networks
- Scope: Universal Active

**Topic resources**

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