Neural Decoder

Neural decoders are machine learning models designed to interpret complex data, primarily focusing on decoding signals from noisy channels or neural activity. Current research emphasizes improving decoder speed and accuracy for applications like brain-computer interfaces and 6G communication, exploring architectures such as transformers, recurrent neural networks (like GRUs), and Gaussian processes, as well as novel training techniques like boosting and equivariant methods. These advancements are significant for enhancing the reliability of communication systems and enabling more sophisticated brain-computer interfaces with reduced computational demands, paving the way for real-world applications in diverse fields.