How does the brain represent different modes of information? Can we design a
system that automatically understands what the user is thinking? Such questions
can be answered by studying brain recordings like functional magnetic resonance
imaging (fMRI). As a first step, the neuroscience community has contributed
several large cognitive neuroscience datasets related to passive
reading/listening/viewing of concept words, narratives, pictures and movies.
Encoding and decoding models using these datasets have also been proposed in
the past two decades. These models serve as additional tools for basic research
in cognitive science and neuroscience. Encoding models aim at generating fMRI
brain representations given a stimulus automatically. They have several
practical applications in evaluating and diagnosing neurological conditions and
thus also help design therapies for brain damage. Decoding models solve the
inverse problem of reconstructing the stimuli given the fMRI. They are useful
for designing brain-machine or brain-computer interfaces. Inspired by the
effectiveness of deep learning models for natural language processing, computer
vision, and speech, recently several neural encoding and decoding models have
been proposed. In this survey, we will first discuss popular representations of
language, vision and speech stimuli, and present a summary of neuroscience
datasets. Further, we will review popular deep learning based encoding and
decoding architectures and note their benefits and limitations. Finally, we
will conclude with a brief summary and discussion about future trends. Given
the large amount of recently published work in the `computational cognitive
neuroscience' community, we believe that this survey nicely organizes the
plethora of work and presents it as a coherent story.

研究通过功能性磁共振成像对大脑的记录，分析了编码模型和解码模型的应用，尤其关注深度学习算法的效果、好处和限制，并总结了神经科学数据集的代表性研究。

深度神经网络与脑部对齐：脑编码与解码（调研）

Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding  (Survey)

Encoding and decoding models are widely used in systems, cognitive, and
computational neuroscience to make sense of brain-activity data. However, the
interpretation of their results requires care. Decoding models can help reveal
whether particular information is present in a brain region in a format the
decoder can exploit. Encoding models make comprehensive predictions about
representational spaces. In the context of sensory systems, encoding models
enable us to test and compare brain-computational models, and thus directly
constrain computational theory. Encoding and decoding models typically include
fitted linear-model components. Sometimes the weights of the fitted linear
combinations are interpreted as reflecting, in an encoding model, the
contribution of different sensory features to the representation or, in a
decoding model, the contribution of different measured brain responses to a
decoded feature. Such interpretations can be problematic when the predictor
variables or their noise components are correlated and when priors (or
penalties) are used to regularize the fit. Encoding and decoding models are
evaluated in terms of their generalization performance. The correct
interpretation depends on the level of generalization a model achieves (e.g. to
new response measurements for the same stimuli, to new stimuli from the same
population, or to stimuli from a different population). Significant decoding or
encoding performance of a single model (at whatever level of generality) does
not provide strong constraints for theory. Many models must be tested and
inferentially compared for analyses to drive theoretical progress.

本文探讨编码模型和解码模型在神经科学中的应用以及它们在代表空间预测、特征贡献等方面的问题。同时，文章也指出单一模型的表现无法为理论提供足够的支撑，需要比较其他模型才能推动理论进展。