How they work, what are their similarities and differences.

In today's world, artificial intelligence (AI) has become an inseparable part of everyday reality. Almost everyone is interested in AI, many already collaborate with neural networks in various fields, but few truly understand how it works.

In the history of technological advancement, we rarely encounter a phenomenon that simultaneously mimics our biological essence and serves as a powerful tool for human thought. Neural networks are precisely such a phenomenon—inspired by the structure of the human brain yet functioning based on their own unique principles.

When we use conversational models, image-generation software, or smart assistants, we often fail to realize that these technologies are built upon a simplified model of our own cognitive mechanisms. However, there are both fundamental similarities and significant differences between artificial and natural intelligence. This article aims to provide a deeper understanding of the mechanisms behind the human brain and artificial neural networks, their interconnections, and their differences. By understanding how these systems operate, we can not only use these advanced technologies more effectively but also gain profound insights into the mysterious nature of our own brain and consciousness.

The Human Brain and Neural Networks․
A Comparative Analysis

Now, we will present the working mechanisms of the human brain and neural networks, as well as their similarities and differences.

The Functioning of the Human Brain

The human brain consists of approximately 86 billion neurons, which are interconnected through trillions of synapses. Here is how it functions:

1. Neurons- The fundamental structural units of the brain. Each neuron consists of a cell body, dendrites, and an axon.
2. Synapses– The connections between neurons, through which information is transmitted.
3. Signal Transmission– When a neuron receives enough stimuli, it "fires," sending an electrical signal (action potential) through the axon to other neurons.
4. Neurotransmitters– Chemical substances that transmit signals between neurons through synapses.
5. Plasticity– The brain can strengthen or weaken synaptic connections depending on their frequency of use (Hebbian rule: "Neurons that fire together, wire together").
6. Modularity– The brain is divided into regions, each responsible for specific functions (e.g., the hippocampus for memory, the amygdala for emotions).

The Functioning of the Neural Networks

Artificial neural networks (ANNs) are modeled after the human brain but are significantly simplified. As we will see later, they do not operate in an energy-efficient manner compared to the biological brain. Here’s how they work:

1. Artificial Neurons– Mathematical functions that receive input data, process it with assigned weights, and produce an output.
2. Layers– Neural networks typically consist of an input layer, hidden layers, and an output layer.
3. Weights and Thresholds– Each connection has a "weight" determining its influence, and each neuron has a "threshold" that must be exceeded for activation.
4. Activation Functions- Without activation functions, a neural network would simply be multiple layers of linear regression, unable to model complex nonlinear dependencies. Activation functions determine how an artificial neuron should respond to input data (e.g., sigmoid, ReLU, Tanh).
5. Learning– Neural networks "learn" by adjusting their weights and thresholds based on the difference between obtained and desired outcomes.
6. Backpropagation– An algorithm used to compute how to adjust weights to minimize errors.

Key Similarities

1. Parallel Processing– Both systems can process large amounts of data simultaneously.
2. Learning Ability– Both can form new connections and adapt to new information.
3. Pattern Recognition– Both are excellent at identifying patterns in data.

📌 Interesting Fact – The human brain can solve problems even with incomplete or incorrect information, which remains a challenge for artificial systems.

Major Differences

Despite their similarities, the human brain and neural networks differ significantly in structure and function:

1. Complexity– The human brain is vastly more complex, with trillions of connections, while artificial neural networks typically have millions or billions.
2. Energy Efficiency– The brain consumes only about 20 watts of power, whereas artificial neural networks with similar computational capacity may require megawatts.
3. Learning Method– The brain learns without direct supervision, while most artificial neural networks rely on supervised learning.
4. Adaptability– The brain can adjust to new tasks with very few examples, whereas artificial neural networks usually require massive datasets.
5. Consciousness– The brain possesses consciousness and self-awareness, while artificial neural networks do not—at least not yet.

📌 Interesting Fact – Even if half of the human brain is removed, it can still recover many of its functions due to neuroplasticity.

Thus, both the human brain and neural networks are powerful mechanisms for data processing and decision-making. While neural networks can outperform the brain in certain tasks, such as analyzing large volumes of data, they are still far from matching the flexibility, adaptability, and efficiency of natural intelligence. The study of the brain continues to inspire the development of new technologies, and in the future, neural networks may become more self-regulating and versatile.

Undoubtedly, the combination of these two systems—the biological stability and flexibility of the brain, coupled with the efficiency of artificial intelligence—could lead not only to innovative solutions but also to groundbreaking discoveries across a wide range of fields.