How does the brain represent different modes of information? Can we design a system that can automatically understand what the user is thinking? We can make progress towards answering such questions by studying brain recordings from devices such as functional magnetic resonance imaging (fMRI). The brain encoding problem aims to automatically generate fMRI brain representations given a stimulus. The brain decoding problem is the inverse problem of reconstructing the stimuli given the fMRI brain representation. Both the brain encoding and decoding problems have been studied in detail in the past two decades and the foremost attraction of studying these solutions is that they serve as additional tools for basic research in cognitive science and cognitive neuroscience. Recently, inspired by the effectiveness of deep learning models for natural language processing and computer vision, such models have been applied for neuroscience as well. In this tutorial, we plan to discuss different kinds of stimulus representations, and popular encoding and decoding architectures in detail. The tutorial will provide a working knowledge of the state of the art methods for encoding and decoding, a thorough understanding of the literature, and a better understanding of the benefits and limitations of encoding/decoding with deep learning.

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?