Neural network diagram showing hidden neuron activations forming a continuous line, illustrating how activation patterns crea

Editorial illustration for Combining hidden neurons still yields a line, highlighting activation's role

Combining hidden neurons still yields a line,...

Combining hidden neurons still yields a line, highlighting activation's role

By AI Daily Post Edited by Brian Petersen, Editor-in-Chief

June 22, 2026 • 2 min read

Large language models are, at their core, heavily trained neural networks. Yet the jargon—hidden layers, activation functions, image versus text data—can feel like a wall. I found myself asking: how does a neural network actually learn when the input is stripped down to its simplest form?

That question drove this piece. Instead of diving straight into convoluted image sets or massive text corpora, we start with a tiny, clean dataset. By building a network from scratch, we can watch each neuron fire, see how layers stack, and discover why piling on linear neurons never gets you past a straight line.

The real kicker? Activation functions. They’re the missing piece that lets a model capture the twists and turns hidden in real‑world data.

Throughout, we’ll walk through the mechanics, step by step, so the fundamentals become clear before the complexity ever arrives. Here’s the thing: understanding the basics makes the later “big AI” stuff far less intimidating.

Therefore, even though we combined the outputs of multiple hidden neurons, the final result is still a line. This brings us to the most important concept in deep learning and neural networks: Activation Functions. Activation Functions Here, our data follows a non-linear pattern, but a straight line can only model linear relationships.

No matter how many linear neurons we combine, the output is still a linear function. Then how do these neural networks learn complex patterns such as curves, shapes, images, and text? We passed the outputs of the two hidden neurons directly to the output layer.

But instead of passing them directly to the output layer, the hidden neurons' outputs are first transformed using a special function called an activation function. It introduces non-linearity into the network, allowing it to learn complex patterns.

Neural Networks, Explained for Beginners: Start Here If They’ve Confused You - Towards Data Science

Why this matters

We’ve seen that stacking hidden neurons without non‑linear steps still produces a straight line, underscoring why activation functions are indispensable. For developers, this means that adding layers alone won’t grant expressive power; the choice of activation dictates whether a model can capture the curvature in real‑world data. Founders should note that the hype around deeper networks can be misleading if the architecture lacks proper non‑linearities—performance gains may be illusory.

Researchers are reminded that the mathematics behind activations remains a focal point; it’s unclear whether newer functions will consistently outperform the classics across tasks. Our understanding of how these functions interact with varied data types—images, text, or mixed inputs—is still evolving, and empirical testing remains essential. In practice, we must treat activation design as a core engineering decision rather than an afterthought, and we should remain cautious about assuming depth alone solves complexity.

Combining hidden neurons still yields a line,...

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