Neural Encoding

Neural encoding investigates how information is represented in neural systems, both biological and artificial. Current research focuses on improving encoding methods for various data types (images, speech, language) using diverse architectures like convolutional and transformer networks, and exploring how encoding impacts performance in tasks such as memory recall, architecture search, and system identification. These advancements are crucial for improving artificial intelligence models, enhancing our understanding of brain function, and developing more efficient neuromorphic computing systems. The development of open-source encoders and efficient hardware accelerators further expands the accessibility and applicability of this research.