👉 Channel computing is a fundamental concept in neural network architectures, particularly in the context of recurrent neural networks (RNNs) and their variants like Long Short-Term Memory (LSTM) networks. It refers to the process of transmitting information through discrete channels within a network, where each channel represents a specific type of data flow or transformation. These channels can be thought of as pathways that carry information from one layer to another, allowing the network to process sequential data by maintaining and updating internal states. Each channel typically has its own set of weights and biases, enabling the network to learn complex patterns and dependencies in the data. By passing information through these channels, the network can effectively model temporal dynamics and relationships, making channel computing essential for tasks involving time-series data, natural language processing, and other sequential data applications.
