What technology is designed to simulate neural networks in humans and animals?

The technology that is specifically designed to simulate neural networks in humans and animals is Artificial Neural Networks. This is a subset of artificial intelligence that attempts to replicate the way human and animal brains process information. By creating layers of interconnected nodes (or "neurons"), artificial neural networks can learn from data, recognize patterns, and make predictions much like biological neural networks.

Artificial neural networks utilize a structure and function concept inspired by the biological networks. They enable systems to learn from experiential data, improving their accuracy over time through a process known as training. This process involves adjusting the weights and biases of connections between nodes to minimize the difference between the predicted output and the actual output.

While artificial intelligence is a broader discipline that encompasses various methods, including rule-based systems and algorithms that do not mimic biological processes, it is not limited to the neural network approach. Machine learning algorithms and deep learning systems also fall under the AI umbrella, with deep learning representing advanced neural networks that utilize many layers (hence "deep") for even greater complexity and accuracy. However, the foundational simulation of biological neural networks specifically relates to artificial neural networks.