Explaining Neural Networks to Students: Everyday Analogies

Neural networks, despite their rapid ascent in the world of artificial intelligence, remain one of the most misunderstood concepts among non-specialists. For educators tasked with introducing neural networks to students, the challenge often lies in bridging the gap between abstract mathematical constructs and tangible, everyday experiences. By employing familiar analogies such as pizza toppings, city maps, and domino chains, we can illuminate the inner workings of neural networks in a way that sparks curiosity and fosters genuine comprehension.

The Building Blocks: Layers as Pizza Toppings

Imagine ordering a pizza. The base is always the same—a doughy foundation. What makes each pizza unique are the layers of toppings: sauce, cheese, vegetables, meats. Each layer adds complexity and flavor, transforming the simple dough into a culinary masterpiece. In much the same way, a neural network is built from layers, each contributing to the network’s capacity to learn and process information.

Input Layer: The first layer is like the pizza dough. It doesn’t do much on its own but provides the essential starting point. In a neural network, the input layer receives raw data, such as pixels from an image or words from a sentence.

Hidden Layers: Next come the toppings—each layer of sauce, cheese, and vegetables. In neural networks, these are called hidden layers. They take the basic ingredients (the input data) and, through a series of transformations, extract features and patterns. Just as adding mushrooms or olives changes the taste, each hidden layer modifies the data in specific ways, making the final result richer and more nuanced.

Output Layer: Finally, the cooked pizza emerges from the oven—ready to eat. The output layer produces the answer: perhaps the classification of an image, or the translation of a sentence. Without the preceding layers, the output would be bland and meaningless.

The artistry of a neural network, like a perfect pizza, lies in the interplay of its layers. Each one builds upon the last, shaping raw ingredients into something meaningful.

Weights and Connections: Mapping Through a City

Now, let’s turn to city maps. Navigating a city involves more than just knowing where you are and where you want to go. The real challenge is finding the best route, considering traffic, road quality, and shortcuts. In neural networks, the “roads” between layers are called connections, and each has a specific weight—much like the varying conditions of city streets.

Picture the input layer as neighborhoods at the city’s edge. Each neighborhood (input neuron) is connected to many other neighborhoods (neurons in the next layer) via roads (connections). Some roads are wide and fast, others narrow and slow. The weight of a connection determines how much influence one neuron’s information has on another—just as a smooth, direct highway makes it easier to travel from one neighborhood to another.

When a neural network is learning, it’s as if the city is constantly adjusting its roads. If a particular route proves useful in reaching the destination efficiently, the network “widen the road” by increasing the weight. If not, the road narrows or even closes. Over time, the city’s map adapts, offering the best possible routes for the information to travel from input to output.

Understanding neural networks as evolving city maps helps students grasp the importance of weights: they are not static, but dynamic routes shaped by experience.

Activation Functions: The Domino Effect

Finally, let’s consider domino chains. Imagine setting up an elaborate pattern of dominos. Each domino represents a neuron. When you push the first domino, the energy travels down the line, but only if each domino is spaced correctly and the force is sufficient. In neural networks, this is the role of the activation function.

Not every neuron should “fire” just because it receives some input. The activation function acts as a gatekeeper, deciding whether the signal is strong enough to pass along—just as a domino will only fall if it’s hit with enough force and at the right angle. Some activation functions are simple thresholds (like requiring a minimum push), while others are more complex, akin to dominos with springs or weights that need a certain amount of energy to topple.

This mechanism enables neural networks to introduce non-linearity, allowing them to model complex relationships and patterns in the data. Without activation functions, the network would be like a row of dominos that only fall in a straight line—incapable of capturing the twists and turns of real-world problems.

Putting It All Together: A Neural Network in Everyday Terms

Let’s imagine you’re trying to teach a computer to recognize handwritten numbers. The data—images of numbers—enter the network as the pizza dough at the input layer. As they pass through hidden layers (the toppings), the network adds complexity, transforming raw pixels into shapes and curves. The connections (city roads) between neurons decide how much influence each pixel has on the final outcome, and the activation functions (dominos) determine whether or not to pass the information forward. Finally, the output layer identifies which number was written, much like tasting the finished pizza.

By connecting abstract concepts to everyday experiences, educators create memorable learning moments that demystify neural networks for students.

Practical Classroom Implementation: A 20-Minute Lesson Outline

To translate these analogies into effective teaching, a structured lesson plan is essential. Below is a sample 20-minute outline designed for upper secondary or undergraduate students, adaptable for various classroom contexts.

1. Introduction (2 minutes)

Begin by asking students if they have ever used a navigation app, ordered a pizza, or set up dominos. Briefly explain that today’s lesson will use these familiar experiences to unravel the mystery of neural networks.

2. Layer Analogy: Pizza Toppings (5 minutes)

• Show a basic pizza and ask students to suggest different topping combinations.
• Compare each layer of toppings to layers in a neural network, explaining how each adds new information.
• Ask students to brainstorm what “layers” might mean in a digital context.

3. Weights Analogy: City Maps (5 minutes)

• Display a simple city map with multiple routes from point A to B.
• Discuss how certain streets (connections) are faster or slower, introducing the concept of weights.
• Invite students to consider how a navigation app might learn which routes are best over time (learning process).

4. Activation Analogy: Domino Chains (5 minutes)

• Set up a short row of dominos (physically or using a video demonstration).
• Illustrate how only a properly spaced domino will cause the chain to fall, mirroring the role of activation functions.
• Relate this to how neurons decide whether to send their signal forward in a network.

5. Synthesis and Application (3 minutes)

• Combine the analogies: trace an example input (a pizza order, a trip across the city, a domino push) through the network.
• Encourage students to repeat the process with their own examples, reinforcing the connections between the analogies and neural network components.

Addressing Common Questions and Misconceptions

As students encounter neural networks for the first time, several questions and misconceptions often arise. Addressing these directly can deepen understanding and build confidence.

Are neural networks intelligent in the human sense?

Neural networks are powerful tools, but they do not possess consciousness or understanding. They detect patterns and make predictions based on data, not intuition or reasoning. Comparing them to city maps or pizza recipes helps clarify that they follow instructions and learn from examples, but they do not “think” as humans do.

Why are so many layers needed?

Just as complex pizza flavors require multiple toppings and a city might have layers of infrastructure (roads, subways, bike paths), deeper networks can capture more intricate relationships in data. However, more layers also mean more computational resources and a greater risk of overfitting—the network might “memorize” rather than “generalize.”

How do neural networks learn?

During training, the network adjusts its weights (road widths) based on feedback, gradually improving its “routes” from input to output. This is analogous to a city planner observing traffic patterns and updating road maps for greater efficiency.

What are activation functions, really?

Activation functions allow neural networks to model non-linear relationships. Without them, the network would be limited to simple, straight-line (linear) reasoning. By introducing thresholds and curves—like tricky domino setups—they enable the network to tackle complex, real-world problems.

Addressing student uncertainties with patience and clear analogies fosters a deeper, more resilient understanding of neural networks.

Legal and Ethical Considerations for Educators

As neural networks become increasingly prevalent in educational technology, European educators must also consider the regulatory landscape. Recent EU legislation, such as the Artificial Intelligence Act, outlines requirements for transparency, fairness, and accountability in AI systems.

Transparency: When teaching about neural networks, emphasize the importance of understanding how decisions are made. Black-box systems—where the reasoning behind outcomes is hidden—pose challenges for accountability and trust.

Fairness: Neural networks can inadvertently perpetuate biases present in their training data. Encourage students to question the data sources and consider how diverse, high-quality data can promote fairness and inclusion.

Accountability: Teachers should highlight the need for ongoing monitoring and assessment of AI systems, especially when they impact student learning or assessment. Discussing the ethical responsibilities of AI developers and users equips students to become thoughtful contributors to the field.

Cultivating an ethical mindset in parallel with technical skills prepares students to navigate the evolving landscape of AI with integrity and care.

Enriching Knowledge Through Playful Exploration

Bringing neural networks to life through pizza, city maps, and dominos transforms a complex subject into a playful and memorable exploration. These analogies not only demystify the technology but also encourage students to draw connections between their lived experiences and cutting-edge innovation.

Encourage experimentation. Invite students to invent their own analogies—perhaps likening neural networks to musical compositions, sports strategies, or gardening. This creative process deepens understanding and personalizes learning.

Foster inquiry. Create space for questions and experimentation. Allow students to build simple neural network models using online tools or paper exercises, reinforcing the concepts introduced through analogies.

Nurture curiosity. Emphasize that neural networks are not magical or impenetrable, but rather systems shaped by human ingenuity and experience. By grounding their understanding in everyday phenomena, learners gain both confidence and inspiration to pursue further study in artificial intelligence.

Teaching neural networks through everyday analogies is not just a pedagogical strategy—it is an invitation for students to see themselves as explorers in the ever-expanding world of intelligent systems.