👉 Channel computing is a paradigm within the field of neural networks that focuses on processing information through interconnected channels or streams, rather than traditional fully connected layers. In channel computing, each neuron in a layer is connected to multiple neurons in the next layer, forming a network of parallel pathways. This approach allows for more efficient and effective feature extraction by enabling the network to capture diverse and complex patterns within data. Each channel typically processes a subset of input features, and the outputs from these channels are aggregated to produce the final output. This architecture is particularly beneficial for tasks involving sequential data, such as time series analysis or natural language processing, where capturing temporal dependencies and interactions between different parts of the data is crucial. By leveraging channel computing, models can achieve better performance and scalability while maintaining a more biologically plausible representation of information flow.