Author: Chen Zhiyan
This article is about 2500 words long and is recommended for a 7-minute read.
The future of neurons is full of infinite possibilities.
In this era of information explosion, have you ever dreamed of conversing fluently with machines or having AI assist you in crafting stunning articles? All of this relies on the magical power of large language models. Today, let us unveil this layer of mystery and embark on a scientific journey into large language models!
When exploring the mysteries of artificial intelligence, we often hear the term “artificial neural networks.” Artificial neural networks are computational models that mimic the structure and function of the brain’s nerve cells (neurons) and are one of the most powerful tools in modern machine learning. In this article, we will delve into the neurons in artificial neural networks, understand how they work, and how they help machine learning models perform complex tasks.
Artificial neural networks simulate the complex operations of biological neural systems, with a large number of neurons interconnected in complex networks. Typically, each neuron connects with thousands of other neurons, forming hundreds of billions of connections. The emergence of intelligent behavior stems from countless interactions between these interconnected neural units.
Neurons are the basic functional units of artificial neural systems, connecting and communicating through electrical signals. A typical neuron consists of three main parts: dendrites, cell body, and axon. Dendrites receive signals from other neurons, the cell body contains the nucleus and other organelles, and the axon is responsible for transmitting signals to other neurons or muscle cells.
The simplest artificial neural model consists of a single neuron, which Star-Trek named the perceptron. It was invented by Frank Rosenblatt in 1957 and includes a simple neuron that calculates the weighted sum of inputs (analogous to the dendrites in biological neurons) using a mathematical function and outputs its result (the output corresponds to the axon of biological neurons), as shown in Figure 1.
Figure 1: Schematic Diagram of Neuron Structure (Source: Internet)
An image of a single neuron, with inputs on the left, multiplied by the weight of each input, the neuron applies a function to the weighted sum of inputs and outputs the result.
2. Neural Network Modeling
In artificial neural networks, neurons act as computational units similar to biological neurons, referred to as artificial neurons or nodes in the neural network. Each node receives input signals, processes them through a weighted summation process, and then outputs a signal. This process can be described in the following steps:
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Input: Each neuron receives multiple inputs from the previous layer of neurons.
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Weighting: Each input has an associated weight that determines the importance of the input signal.
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Summation: The neuron sums all weighted inputs to obtain a total.
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Activation Function: The total is processed through a nonlinear activation function to introduce nonlinearity, enabling the network to learn and simulate complex functions.
The activation function is a key component in neural networks, determining whether a neuron should be activated. Without an activation function, regardless of how the input changes, the network’s output will be linear, which greatly limits the network’s capabilities. Common activation functions include:
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Sigmoid: Outputs values between 0 and 1, suitable for binary classification problems.
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Tanh: Outputs values between -1 and 1, similar to Sigmoid but with a wider range of output values.
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ReLU (Rectified Linear Unit): Outputs non-negative values and is computationally efficient, making it one of the most popular activation functions today.
3. Layer Structure of Neural Networks
In neural networks, individual neurons are stacked to form layers of varying sizes, which can be placed sequentially adjacent to each other, making the network deeper. Neural networks consist of multiple layers, including the input layer, hidden layers, and output layer. Each layer consists of multiple neurons:
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Input Layer: Receives raw data as input.
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Hidden Layer: Intermediate layer that processes data from the input layer and passes it to the next layer.
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Output Layer: Produces the final output result.
When constructing neural networks in this manner, neurons that do not belong to the input or output layers are considered hidden layers, as their name describes: hidden layers are a black box model, which is one of the main features of artificial neural networks. The layer structure of neural networks is shown in Figure 2:
Figure 2: Layer Structure of Artificial Neural Networks
4. Training Neural Networks
Neural networks are composed of simple neurons that operate in parallel, and the connections between neurons play a crucial role, determining the network’s functionality. The interconnected neurons influence the output through their respective weights, which can be appropriately adjusted during the training phase of the neural network.
Training a neural network is an optimization process aimed at adjusting the weights of all neurons in the network so that it can accurately perform specific tasks. This process typically involves the following steps:
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Forward Propagation: Data is propagated forward through the network to compute the predicted output.
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Loss Calculation: The difference (loss) between the predicted output and the actual output is computed.
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Backward Propagation: Gradients are calculated based on the loss, and weights are updated to minimize the loss.
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Iterative Optimization: Forward propagation and backward propagation are repeated until the network’s performance reaches a satisfactory level.
5. Applications of Artificial Neural Networks
Artificial neural networks have applications in many fields, including but not limited to:
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Medical Diagnosis: Assisting in diagnosis by analyzing medical images.
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Autonomous Vehicles: Recognizing road signs and obstacles.
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Voice Assistants: Understanding and responding to natural language commands.
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Recommendation Systems: Recommending products or content based on user behavior.
The neurons in artificial neural networks are fundamental to understanding and building intelligent systems. By mimicking the workings of the human brain, we can create machine learning models that perform complex tasks. With technological advancements, artificial neural networks will play an increasingly important role in the future development of artificial intelligence.
