What is a Neural Network?

What is a Neural Network?

A neural network is a learning computer model composed of interconnected units that process information in a way loosely inspired by the structure of the human brain.

Neural networks are a central method of machine learning within AI. They allow computer systems to recognize patterns in data, analyze complex relationships, and make decisions based on this analysis. They are particularly powerful in the form of so-called deep learning models, which consist of many layers and can also process unstructured data such as images, text, or audio signals.

Modern AI applications such as large language models (LLMs), image generators, or predictive analytics tools are largely based on neural networks. Foundation models also fundamentally rely on neural networks, usually in the form of so-called transformer models.

Background and Context

The origins of neural networks go back to 1943, when attempts were first made to mathematically model the signal processing of the human brain. In the 1950s and 1960s, computer scientists experimented with simple network structures, but limited computing power and insufficient data availability prevented practical implementation. Interest in the method declined. In the 1980s, the concept was revisited and technically refined. From the 1990s onwards, neural networks increasingly entered AI research. However, they only reached their current state of development thanks to advances in data processing, access to large training datasets, and powerful computer infrastructure.

 Only these technical conditions made it possible to train complex models that could mimic or even surpass human cognitive abilities. A major breakthrough was the development of the transformer architecture in 2017. Today, it forms the foundation of many modern AI systems, such as in natural language processing or image analysis.

How Does a Neural Network Work?

A neural network consists of a multitude of interconnected nodes. These nodes receive information, process it using mathematical functions, and pass the result on to the next layer. The flow of information occurs layer by layer, with each layer fulfilling a specific function.

Typically, a neural network is structured into three key layers:

• Input Layer: This is where the raw data enters the network. Depending on the application, this may be image pixels, text tokens, or numerical values.
• Hidden Layers: This is where the actual computations take place. The artificial neurons analyze the input data, assign weights, and decide—based on so-called activation functions—what information should be passed on. Depending on the model’s complexity, there may be one or many of these layers.
• Output Layer: At the end of the network, the result is produced. This can take different forms depending on the application, for example, as a classification (e.g., “image shows a cat”), as a numerical prediction, or, in the case of language models, as generated text.

Through training, the network learns which weightings lead to correct results. During this process, the network is fed with example data, gradually adjusts its internal parameters, and thereby continuously improves its performance.

Application at INFORM

INFORM uses neural networks in its solutions for sales forecasting and replenishment in the fresh food trade. In assortments with fluctuating demand, incomplete data, and seasonal effects, they identify non-linear relationships and generate robust forecasts.

A typical example is the forecasting of seasonal products such as asparagus or apples, where demand can shift within a very short time depending on calendar week, origin, regionality, price, or promotion patterns. The networks learn to capture such interactions, complement missing information through generalized structures and continuously adapt their behavior to new conditions. This enables daily forecasts at the product or product-group level. These forecasts form the basis for automated ordering decisions. Unlike rigid rule-based systems, the models weigh factors context-sensitively, e.g., whether a sales increase is due to price, seasonality, or assortment changes. In combination with other optimization methods, differentiated order proposals are generated. Planners intervene only where human judgment is required, such as in the case of supply shortages or regional expertise.

Advantages of Neural Networks

• Recognition of complex relationships: They identify patterns and dependencies that classical models cannot detect, especially in non-linear developments.
• Learning capability: Neural networks automatically adapt to new data and changing conditions through continuous training.
• Handling incomplete data: Even when information is missing or incomplete, they can leverage generalized structures to produce valid results.
• Automation of data-driven decisions: They help relieve professionals, for example by generating automated forecasts or order proposals.
• Versatile applicability: Whether in forecasting, classification, text, or image processing, neural networks can be flexibly integrated into a wide range of applications.

Challenges in Using Neural Networks

• High data requirements: Large volumes of high-quality training data are necessary for reliable forecasting accuracy.
• Intransparency of decisions: Internal calculations are difficult to interpret, making explainability and acceptance more challenging (the “black box problem”).
• High computational effort: Training and running complex models require powerful hardware and appropriate infrastructure.
• Risk of faulty weighting: Biases in the dataset or erroneous correlations can lead to incorrect forecasts.
• Maintenance and upkeep: Models must be regularly monitored, validated, and adjusted to changing conditions.

FAQ On Neural Networks

How do neural networks work compared to classical algorithms?

Neural networks do not operate with fixed, predefined rules but instead learn from example data. While classical algorithms follow defined instructions, neural networks independently recognize patterns and dependencies in order to make decisions or generate predictions.

What types of neural networks exist?

There are different types of neural networks designed for various tasks:

• Feedforward networks pass information in only one direction and are well-suited for simple classification or prediction tasks.
• Convolutional Neural Networks (CNNs) are particularly effective in image processing. They detect patterns such as edges or shapes and are used in applications like facial recognition or medical image analysis.
• Recurrent Neural Networks (RNNs) specialize in sequential data such as speech or time series. They retain earlier inputs in memory and are well-suited for language models or forecasts of developments over time.

How are neural networks trained?

Neural networks are trained using training data. The network is given many examples from which it learns to recognize patterns and establish relationships. In each iteration, it compares its prediction with the actual outcome and adjusts its internal weights to minimize error. This process—usually over many iterations—is called training. The more data and computing power are available, the better the network can learn and generalize.

Conclusion

Neural networks are the central tool of modern AI and hold great potential for solving complex tasks in an automated and intelligent way. Thanks to their structured learning capability, they open up new paths toward more efficient processes, well-founded decisions, and innovative applications. We are only at the beginning of a development that will sustainably transform both business and society. The potential of neural networks is enormous and it grows with every new application.

 Would you like to learn more about how AI solutions can be applied in your company? Then take a look at our other blog articles or get in touch with us directly.