We compiled the information in this article based on the questions and messages we received. Our goal is to help readers who don’t have a technical background understand the reasoning and workings behind our project. For those interested in the technical details, more in-depth information and further reading are available on our website, which we highly recommend visiting.

Thousands of years of evolutionary processes have shown us that emotions did not emerge solely for social interactions, but as a fundamental mechanism for survival. The adrenaline that surges while escaping a predator narrows our attention and sharpens our focus. The dopamine that follows success motivates us to learn new things and take risks. Even emotional states that may seem negative such as depression carried evolutionary advantages by pushing us to dwell on events, allowing for deeper analysis and more complex connections. This is precisely why emotions are among the greatest tools of creativity and intuition, enabling the mind to go beyond the limits of existing data sets.

The Synthetic Cortex project we are developing today is inspired by this very insight. Our aim is not only to enhance AI language models with computational power, but to add synthetic emotions and instinctive behavioral patterns into their reasoning. Because we know that emotions cannot be separated from decision-making. In the human mind, without emotional drivers like motivation, stress, or curiosity, new ideas simply do not emerge. Likewise, creativity and the ability to form unexpected connections are only possible against this emotional backdrop. With Scortex L1, we took the first step. This version was able to apply emotional modulation only at the output layer. However, this was merely a superficial effect.

With L2, emotions no longer influence results from the outside; they are woven directly into the reasoning process. Emotional loads are now embedded in the hidden layers, where decisions are formed at the deepest level. As a result, the AI no longer responds only to context, but also to its internal states and simulated past experiences.

How do we achieve this? By mathematically modeling the functions of hormones and neurotransmitters in the human brain. Cortisol, dopamine, serotonin, these biological factors shaped our decision-making throughout evolution, and we are adapting their functional roles into machines. Of course, this is not a one-to-one replica of the brain. Instead of recreating its biological complexity, we translate the effects of these hormones into what we call “emotional loads” and embed them within artificial neural networks. The result is not a system that feels, but a system whose reasoning can be dynamically guided by emotions.

This is where the significance of our approach lies. Simply scaling up datasets will not lead us to general intelligence. What is missing is the emotional layer that fuels human creativity. Synthetic Cortex transforms AI from a mechanical calculator into a system capable of stepping beyond the dataset, making intuitive and creative decisions. This is not just determinism, it is a step toward something closer to organic intelligence.

And this is not just vision. Our early results show that the model can now carry different emotional weights under the same stimulus, and consequently make different decisions. In other words, emotions are no longer just simulated; they directly influence the diversity and creativity of decision-making. Scortex L2 is still at the beginning of its journey. But from an evolutionary perspective, we know that emotions were the driving force that shaped the human brain as we know it today. Integrating this same force into AI opens the door to an entirely new future. Perhaps the path to human level intelligence does not lie in more data, but in better emotions.

In fact, the probabilistic nature of artificial neural networks gives us a strong metaphor here. A single neuron’s output holds no meaning by itself; yet, the countless combinations of millions of data points produce coherent inferences during learning. The same principle applies to LLM architectures: they establish probabilistic connections across expanded datasets to generate consistent and meaningful responses. What we are doing is applying a similar mathematical model to emotions. By hybridizing emotional data into the existing structure, we can model human-like decision-making with greater realism. And our first tests show that this approach is not just theoretical, but already delivering successful results in practice.

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