What are computational models inspired by the human brain, consisting of interconnected neurons called?

The term for computational models inspired by the human brain, consisting of interconnected neurons, is artificial neural networks (ANN). These models are designed to simulate the way human brains process information by connecting nodes (or artificial neurons) in layers that work together to interpret data. ANN can learn from data by adjusting the weights of connections between nodes, similar to how synapses in the human brain strengthen or weaken based on experience.

Cognitive architectures refer to broader frameworks intended to simulate human cognitive processes, encompassing reasoning, learning, and memory, rather than focusing solely on the interconnected neuron structure. Deep learning models are a subset of artificial neural networks that use multiple layers (deep networks) to learn complex representations, but the broader term specifically identifying the structure is artificial neural networks. Reinforcement learning systems focus on how agents ought to take actions in an environment to maximize cumulative reward and do not specifically describe the architecture of interconnected neurons. Thus, the clear definition and connection to both brain inspiration and structure make artificial neural networks the correct answer.