Utilizing Deep Learning and Natural Language Processing to Elucidate Neural Activity in Human Listeners

Identifying the neural substrates of human auditory system has been a classic pursuit that fell into the list of grand challenges in neuroscience. Deep learning and natural language processing towards the end of the last decade have given a fruitful way to decipher and interpret neurons concerned with audio perception. This paper describes how these technologies may be used to enhance understanding of neural activity in human listeners. This will begin by defining the general auditory perception and the neural architecture supporting it before proceeding to review the insights learned from applying deep learning and natural language processing to demystify neural signals. Activity that occurs in the human brain is very important in the development of neuroscience, cognitive science and artificial intelligence. Here the effort has been made to try to figure out how CNN, RNN, and NLP could be integrated for the purpose of building a model to study the neural responses evoked by auditory stimuli. Selecting CNNs for feature extraction and RNNs for modeling of the temporal sequence consequently applying advanced elaboration of natural language processing we are aimed to reveal the intricate neural patterns for language and sound processing. As a training and testing dataset we have used Human Connectome Project with 1200 neural images total. It attained a 91.2% accuracy while applying in the Python platform using Keras and tensorflow. Implications and relevance: The results of this work may have significance for further neuroscientific research, the creation of the systems of brain-computer interfaces, and thinking about the problems connected with privacy and consent. The paper demonstrates how deep learning and the NLP application alter the ways in which we look at auditory processing by the brain and future work in neuroscience and more.