What term describes structures of artificial neural networks that define data flow and processing?

The term that accurately describes structures of artificial neural networks that define data flow and processing is "neural architectures." This terminology encompasses the overall design and configuration of the network, including the arrangement of neurons and layers, the types of connections between them, and the methods of data processing and transformation within the network.

Neural architectures are critical in determining how a neural network operates, as they dictate how information travels through the network and how various computations are performed. By defining the structure, each architecture can optimize the performance for specific tasks, such as image recognition, natural language processing, or anything in between, making it a foundational concept in AI and machine learning.

While "neural networks" refers to the overall system comprising interconnected layers of neurons that process inputs to produce outputs, it does not address the specifics of the structure as comprehensively as "neural architectures." "Neural pathways," which may imply routes of information flow, and "neurotransmitters," typically used in the context of biological systems, do not pertain to the defined structures of artificial neural networks. Thus, "neural architectures" stands out as the precise term for describing how these neural systems are structured and function.