First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.

中文翻译：

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使用缓慢的无监督学习在大脑中学习不变的对象和空间视图表示

首先，描述了在下颞叶视觉皮层中学习不变表征的神经生理学证据。这包括在颞叶视觉皮层中具有位置、大小、照明、视图和形态变换不变性的物体和面部表征；颞上沟皮层中的整体物体运动；海马体中的空间视图表示在眼睛位置、头部方向和位置方面是不变的。其次，提出了使大脑能够学习这些不变表示的计算机制。对于腹侧视觉系统，一个关键的适应是在慢速无监督学习中使用环境统计中可用的信息来学习对象的变换不变表示。这与人工神经网络中的深度监督学习形成对比，人工神经网络使用数千个样本进行训练，这些样本被神经元教师强制分为不同的类别。类似的慢速学习原则适用于背侧视觉系统中全局物体运动的学习，该系统导致颞上沟中的皮层。在 VisNet 中探索的学习规则是一个带有短期记忆痕迹的关联规则。前馈架构有四个阶段，每个阶段都收敛。这种类型的缓慢学习是在大脑中在分层组织的竞争性神经元网络中实现的，从一个阶段到另一个阶段会收敛，层次结构中只有 4-5 个阶段。缓慢学习也显示出有助于学习坐标变换，在背侧视觉系统中使用增益调制延伸到顶叶皮层和后皮层。表示是在世界中位置的异中心空间视图坐标中学习的，并且与眼睛位置、头部方向和个人所在的位置无关。这使得海马空间视图细胞能够在视图细节被短时间遮挡时使用愚蠢的、自运动的信号进行导航。