Despite the remarkable strides made in artificial intelligence, current
object recognition models still lag behind in emulating the mechanism of visual
information processing in human brains. Recent studies have highlighted the
potential of using neural data to mimic brain processing; however, these often
reply on invasive neural recordings from non-human subjects, leaving a critical
gap in our understanding of human visual perception and the development of more
human brain-like vision models. Addressing this gap, we present, for the first
time, "Re(presentational)Al(ignment)net", a vision model aligned with human
brain activity based on non-invasive EEG recordings, demonstrating a
significantly higher similarity to human brain representations. Our innovative
image-to-brain multi-layer encoding alignment framework not only optimizes
multiple layers of the model, marking a substantial leap in neural alignment,
but also enables the model to efficiently learn and mimic human brain's visual
representational patterns across object categories and different neural data
modalities. Furthermore, we discover that alignment with human brain
representations improves the model's adversarial robustness. Our findings
suggest that ReAlnet sets a new precedent in the field, bridging the gap
between artificial and human vision, and paving the way for more brain-like
artificial intelligence systems.

通过对非侵入式脑电图记录进行对齐，我们提出了 “Re (presentational) Al (ignment) Net”，这是一个与人类大脑活动对齐的视觉模型，其与人类大脑表征更加相似，从而在人工智能领域取得了突破。

ReAlnet: 通过人脑神经表征对齐实现更加人类大脑类似的视觉

ReAlnet: Achieving More Human Brain-Like Vision via Human Neural  Representational Alignment

Deep neural networks have made tremendous gains in emulating human-like
intelligence, and have been used increasingly as ways of understanding how the
brain may solve the complex computational problems on which this relies.
However, these still fall short of, and therefore fail to provide insight into
how the brain supports strong forms of generalization of which humans are
capable. One such case is out-of-distribution (OOD) generalization --
successful performance on test examples that lie outside the distribution of
the training set. Here, we identify properties of processing in the brain that
may contribute to this ability. We describe a two-part algorithm that draws on
specific features of neural computation to achieve OOD generalization, and
provide a proof of concept by evaluating performance on two challenging
cognitive tasks. First we draw on the fact that the mammalian brain represents
metric spaces using grid-like representations (e.g., in entorhinal cortex):
abstract representations of relational structure, organized in recurring motifs
that cover the representational space. Second, we propose an attentional
mechanism that operates over these grid representations using determinantal
point process (DPP-A) -- a transformation that ensures maximum sparseness in
the coverage of that space. We show that a loss function that combines standard
task-optimized error with DPP-A can exploit the recurring motifs in grid codes,
and can be integrated with common architectures to achieve strong OOD
generalization performance on analogy and arithmetic tasks. This provides both
an interpretation of how grid codes in the mammalian brain may contribute to
generalization performance, and at the same time a potential means for
improving such capabilities in artificial neural networks.

通过探索哺乳动物大脑中的 grid codes 母题点过程，提出了一种由 DPP-A 驱动的关注机制，并以此实现了有趣的泛化能力，这同时提出了一种方法来改善人工神经网络的自举能力。