Local connectivity refers to a characteristic of neural network architectures, particularly convolutional neural networks (CNNs), where each neuron is connected only to a small region of the input data instead of the entire input. This design helps in capturing spatial hierarchies and local features in the data, making it efficient for tasks like image recognition. By focusing on local connections, networks can reduce the number of parameters and computations, improving performance and generalization.
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