Humans and animals exhibit a range of interesting behaviors in dynamic
environments, and it is unclear how our brains actively reformat this dense
sensory information to enable these behaviors. Experimental neuroscience is
undergoing a revolution in its ability to record and manipulate hundreds to
thousands of neurons while an animal is performing a complex behavior. As these
paradigms enable unprecedented access to the brain, a natural question that
arises is how to distill these data into interpretable insights about how
neural circuits give rise to intelligent behaviors. The classical approach in
systems neuroscience has been to ascribe well-defined operations to individual
neurons and provide a description of how these operations combine to produce a
circuit-level theory of neural computations. While this approach has had some
success for small-scale recordings with simple stimuli, designed to probe a
particular circuit computation, often times these ultimately lead to disparate
descriptions of the same system across stimuli. Perhaps more strikingly, many
response profiles of neurons are difficult to succinctly describe in words,
suggesting that new approaches are needed in light of these experimental
observations. In this thesis, we offer a different definition of
interpretability that we show has promise in yielding unified structural and
functional models of neural circuits, and describes the evolutionary
constraints that give rise to the response properties of the neural population,
including those that have previously been difficult to describe individually.
We demonstrate the utility of this framework across multiple brain areas and
species to study the roles of recurrent processing in the primate ventral
visual pathway; mouse visual processing; heterogeneity in rodent medial
entorhinal cortex; and facilitating biological learning.

我们提供了一个新的可解释性定义，展示了产生神经回路响应特性的演化约束，并展示了该框架在多个脑区和物种中研究循环处理在灵长类动物腹侧视觉通路，小鼠视觉处理，啮齿动物介董皮质的异质性以及促进生物学学习的作用。

系统神经科学的目标驱动方法

A Goal-Driven Approach to Systems Neuroscience

The partially occluded image recognition (POIR) problem has been a challenge
for artificial intelligence for a long time. A common strategy to handle the
POIR problem is using the non-occluded features for classification.
Unfortunately, this strategy will lose effectiveness when the image is severely
occluded, since the visible parts can only provide limited information. Several
studies in neuroscience reveal that feature restoration which fills in the
occluded information and is called amodal completion is essential for human
brains to recognize partially occluded images. However, feature restoration is
commonly ignored by CNNs, which may be the reason why CNNs are ineffective for
the POIR problem. Inspired by this, we propose a novel brain-inspired feature
restoration network (BIFRNet) to solve the POIR problem. It mimics a ventral
visual pathway to extract image features and a dorsal visual pathway to
distinguish occluded and visible image regions. In addition, it also uses a
knowledge module to store object prior knowledge and uses a completion module
to restore occluded features based on visible features and prior knowledge.
Thorough experiments on synthetic and real-world occluded image datasets show
that BIFRNet outperforms the existing methods in solving the POIR problem.
Especially for severely occluded images, BIRFRNet surpasses other methods by a
large margin and is close to the human brain performance. Furthermore, the
brain-inspired design makes BIFRNet more interpretable.

提出了一种基于大脑启发的特征恢复网络（BIFRNet），通过模拟视觉神经回路来检测部分遮挡图像，其中包括存储对象先验知识和恢复遮挡特征的完成模块等。在合成和真实的遮挡图像数据集上进行了实验，结果表明 BIFRNet 在解决部分遮挡图像问题方面优于现有的方法，特别是对于严重遮挡的图像。

BIFRNet: 部分遮挡图像识别的脑启发特征恢复深度神经网络

BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition

Converging evidence suggests that the mammalian ventral visual pathway
encodes increasingly complex stimulus features in downstream areas. Using deep
convolutional neural networks, we can now quantitatively demonstrate that there
is indeed an explicit gradient for feature complexity in the ventral pathway of
the human brain. Our approach also allows stimulus features of increasing
complexity to be mapped across the human brain, providing an automated approach
to probing how representations are mapped across the cortical sheet. Finally,
it is shown that deep convolutional neural networks allow decoding of
representations in the human brain at a previously unattainable degree of
accuracy, providing a more sensitive window into the human brain.

利用深度卷积神经网络，研究人员成功地探测到哺乳动物腹侧视觉途径中逐渐增加的特定特征梯度，并将不断增加的特征复杂度映射到人类大脑，从而提供了研究大脑表示如何映射的自动化方法。最终，研究表明，在先前无法实现的精度下，深度卷积神经网络可以对人脑表示进行解码，为我们提供了更敏感的窗口观察人脑。