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In the human brain, neurons are the basic building blocks of the nervous system, and they communicate with each other through electrical and chemical signals:

1. Neurons and Connections: 

   Neurons are specialized cells that transmit information through electrical impulses. Neurons are connected to each other through structures called synapses. At synapses, the sending neuron releases neurotransmitters, which carry chemical signals to the receiving neuron. 

2. Electrical Signals: 

   Neurons generate electrical signals called action potentials. When a neuron receives a strong enough signal from its input connections, it can initiate an action potential, which travels down the neuron's axon, enabling communication with other neurons. 

3. Processing of Input Signals: 

   Neurons process information by integrating input signals from multiple sources. This integration occurs at the neuron's dendrites and cell body. If the overall input surpasses a certain threshold, the neuron fires an action potential. 

4. Activation: 

   When a neuron fires an action potential, it sends an electrical signal down its axon to communicate with other neurons at the synapses. This process allows for the transmission of information throughout the neural network. 

The neural network in a human brain has a vast number of neurons (estimated around 86 billion) and intricates patterns of connectivity. 

The neural networks in artificial intelligence are inspired by the structure and function of the biological brain but are not identical to it. They are designed for specific computational tasks and may differ in their architecture and mechanisms from biological neural networks. 

The equation "Neuron = linear + activation" refers to a simplified representation of a single artificial neuron in a neural network. In neural networks, the basic building block is often called a neuron or a node. The equation breaks down the computation that occurs within a neuron into two main steps: a linear transformation followed by an activation function.

1. Linear Transformation: The linear transformation involves multiplying the input values by weights, summing up the results, and adding a bias. Mathematically, it can be expressed as:

          ------------------------------- [3732a]

where,

          w_i​ are the weights.

          x_i​ are the input values.

          b is the bias.

          n is the number of inputs.

2. Activation Function:

   The output of the linear transformation is then passed through an activation function. The activation function introduces non-linearity to the system, allowing the neural network to learn complex patterns. Common activation functions include sigmoid, hyperbolic tangent (tanh), and rectified linear unit (ReLU), among others. Mathematically, it can be represented as:

          activation = f(linear) ------------------------------ [3732b]

where,

          f is the activation function.

Therefore, the overall equation is:

         Neuron = activation(linear) ----------------------------- [3732c]

This process is repeated for each neuron in a neural network, and the network learns to map input data to desired output through the adjustment of weights and biases during training.

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