Description: A Block Neural Network is a neural network architecture composed of multiple interconnected blocks or modules, each of which performs a specific function in data processing. This modular structure allows for greater flexibility and scalability in the design of neural networks, facilitating the implementation of different types of layers and activation functions. The blocks can be designed to perform specific tasks, such as feature extraction, data normalization, or information combination, optimizing the overall performance of the model. Additionally, this architecture allows for the reuse of blocks in different networks, which can reduce development time and improve efficiency. Block Neural Networks are particularly useful in complex applications where hierarchical processing and integration of multiple information sources are required. Their modular design also facilitates experimentation and research, allowing researchers to test new ideas and approaches without having to rebuild the entire network from scratch.
