![]() ![]() In this tutorial, we plan to discuss different kinds of stimulus representations, and popular encoding and decoding architectures in detail. 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. 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. The brain decoding problem is the inverse problem of reconstructing the stimuli given the fMRI brain representation. ![]() The brain encoding problem aims to automatically generate fMRI brain representations given a stimulus. ![]() ![]() 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). ![]()
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