PhiNets：基于时间预测假说的脑启发非对比学习
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis
AI总结：
受时间预测假说启发，提出PhiNet非对比学习模型，通过双预测器结构提升表示稳定性与适应性，优于SimSiam，并扩展出X-PhiNet以增强持续学习。
摘要：
预测编码是一种理论，它假设大脑皮层在不同抽象层次上预测感觉输入，以最小化预测误差。受预测编码的启发，Chen等人（2024）提出了另一种理论，即时间预测假说，声称海马体中的序列记忆是通过从过去的感觉输入预测输入信号而涌现的。具体来说，他们假设海马体中的CA3预测器在输入信号之间创建了突触延迟，该延迟由随后的CA1预测器补偿。尽管基于时间预测假说复现了记录的神经活动，但其有效性尚未得到充分探索。在这项工作中，我们旨在从自监督学习的角度探索时间预测假说。具体而言，我们关注非对比学习，它生成输入图像的两个增强视图，并从一个视图预测另一个视图。非对比学习与时间预测假说密切相关，因为突触延迟是由StopGradient隐式创建的。基于流行的非对比学习器SimSiam，我们提出了PhiNet，它是SimSiam的扩展，具有分别对应CA3和CA1的两个预测器。通过研究PhiNet模型，我们发现了两个发现。首先，有意义的数据表示在PhiNet中比在SimSiam中更稳定地涌现。这最初由我们的学习动态分析支持：PhiNet对表示崩溃更鲁棒。其次，PhiNet在在线和持续学习场景中更快地适应新出现的模式。对于从业者，我们还提出了一个扩展，称为X-PhiNet，它与动量编码器集成，在持续学习中表现出色。总而言之，我们的工作揭示了时间预测假说在鲁棒性和适应性方面是一个合理的模型。
英文摘要：
Predictive coding is a theory which hypothesises that cortex predicts sensory inputs at various levels of abstraction to minimise prediction errors. Inspired by predictive coding, Chen et al. (2024) proposed another theory, temporal prediction hypothesis, to claim that sequence memory residing in hippocampus has emerged through predicting input signals from the past sensory inputs. Specifically, they supposed that the CA3 predictor in hippocampus creates synaptic delay between input signals, which is compensated by the following CA1 predictor. Though recorded neural activities were replicated based on the temporal prediction hypothesis, its validity has not been fully explored. In this work, we aim to explore the temporal prediction hypothesis from the perspective of self-supervised learning. Specifically, we focus on non-contrastive learning, which generates two augmented views of an input image and predicts one from another. Non-contrastive learning is intimately related to the temporal prediction hypothesis because the synaptic delay is implicitly created by StopGradient. Building upon a popular non-contrastive learner, SimSiam, we propose PhiNet, an extension of SimSiam to have two predictors explicitly corresponding to the CA3 and CA1, respectively. Through studying the PhiNet model, we discover two findings. First, meaningful data representations emerge in PhiNet more stably than in SimSiam. This is initially supported by our learning dynamics analysis: PhiNet is more robust to the representational collapse. Second, PhiNet adapts more quickly to newly incoming patterns in online and continual learning scenarios. For practitioners, we additionally propose an extension called X-PhiNet integrated with a momentum encoder, excelling in continual learning. All in all, our work reveals that the temporal prediction hypothesis is a reasonable model in terms of the robustness and adaptivity.
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