How do humans learn from raw sensory experience? Throughout life, but most obviously in infancy, we learn without explicit instruction. We propose a detailed biological mechanism for the widely embraced idea that learning is driven by the differences between predictions and actual outcomes (i.e., predictive error-driven learning). Specifically, numerous weak projections into the pulvinar nucleus of the thalamus generate top-down predictions, and sparse driver inputs from lower areas supply the actual outcome, originating in Layer 5 intrinsic bursting neurons. Thus, the outcome representation is only briefly activated, roughly every 100 msec (i.e., 10 Hz, alpha), resulting in a temporal difference error signal, which drives local synaptic changes throughout the neocortex. This results in a biologically plausible form of error backpropagation learning. We implemented these mechanisms in a large-scale model of the visual system and found that the simulated inferotemporal pathway learns to systematically categorize 3-D objects according to invariant shape properties, based solely on predictive learning from raw visual inputs. These categories match human judgments on the same stimuli and are consistent with neural representations in inferotemporal cortex in primates.

中文翻译：

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新皮质和 Pulvinar 的深度预测学习。

人类如何从原始感官体验中学习？在整个生命过程中，但最明显的是在婴儿期，我们在没有明确指导的情况下学习。我们为广泛接受的观点提出了一个详细的生物学机制，即学习是由预测和实际结果之间的差异驱动的（即预测错误驱动学习）。具体来说，大量微弱的投射到丘脑的枕核会产生自上而下的预测，来自较低区域的稀疏驱动输入提供实际结果，起源于第 5 层内在爆发神经元。因此，结果表示仅被短暂激活，大约每 100 毫秒（即 10 Hz，alpha），导致时间差异误差信号，驱动整个新皮质的局部突触变化。这导致了一种生物学上合理的错误反向传播学习形式。我们在视觉系统的大型模型中实施了这些机制，发现模拟的颞下神经通路仅基于原始视觉输入的预测学习，根据不变的形状属性学习系统地对 3-D 对象进行分类。这些类别与人类对相同刺激的判断相匹配，并且与灵长类动物颞下皮层的神经表征一致。