神经噪声使罕见事件的内部模拟准确成为可能
Neural noise enables accurate internal simulation of rare events
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- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo（东京大学 UTIAS 神经智能国际研究中心 (WPI-IRCN)）
- Division of Computational Science and Technology, KTH Royal Institute of Technology（瑞典皇家理工学院计算科学与技术系）
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中文总结 AI 辅助
本研究通过贝叶斯置信传播神经网络在马尔可夫链随机游走上的训练与重放，发现适度神经噪声对罕见事件的准确内部模拟至关重要，能补偿有限经验导致的采样误差。
中文摘要 AI 辅助
大脑需要准确的内部世界模型来生成预测并指导行为。然而，它必须从有限的经验中估计环境的统计结构。这对于罕见事件尤其困难，因为在有限样本中观察到的频率可能大幅低估或高估其真实频率。尽管存在这一采样问题，大脑如何构建准确的内部模型仍不清楚。我们使用一种在具有受控事件频率的马尔可夫链随机游走事件序列上训练的贝叶斯置信传播神经网络（BCPNN）来解决这一问题。将底层马尔可夫结构视为真实情况，我们在有限的事件序列样本上训练网络，然后允许其基于学习到的结构生成自主重放。我们在边际事件频率和条件转移结构两个层面上评估重放的保真度。我们发现，适度的神经噪声（在重放期间模拟为单位活动中时间相关的随机波动）对于忠实的内部模拟至关重要。没有这种变异性，确定性重放会系统性地低估或高估罕见事件，而适度的噪声则恢复了其边际和条件出现。适度的噪声还拓宽了产生准确重放的参数值范围，使模型对参数变化更加稳健。总之，这些结果支持噪声辅助的内部模拟作为补偿有限经验引起的采样误差的潜在机制。我们的模型还提供了一个可测试的框架，用于研究在帕金森病等疾病中，神经变异性的改变如何可能损害内部模型的保真度。
英文摘要
The brain needs an accurate internal model of the world to generate predictions and guide behavior. However, it must estimate the statistical structure of the environment from limited experience. This is particularly difficult for rare events, whose observed frequencies in a limited sample may substantially under- or overestimate their true frequencies. How the brain constructs an accurate internal model despite this sampling problem remains unclear. We address this problem using a Bayesian Confidence Propagation Neural Network (BCPNN) trained on event sequences from a Markov-chain random walk with controlled event frequencies. Treating the underlying Markov structure as the ground truth, we train the network on limited sample of event sequences and then allow it to generate autonomous replay based on the learned structure. We evaluate replay fidelity at the levels of both marginal event frequencies and conditional transition structure. We find that moderate neural noise, modeled as temporally correlated random fluctuations in unit activity during replay, is critical for faithful internal simulation. Without this variability, deterministic replay systematically under- or overrepresents rare events, whereas moderate noise restores both their marginal and conditional occurrence. Moderate noise also broadens the range of parameter values that produce accurate replay, making the model more robust to parameter variation. Together, these results support noise-assisted internal simulation as a potential mechanism for compensating for sampling errors arising from limited experience. Our model also provides a testable framework for investigating how altered neural variability may impair internal-model fidelity in disorders such as Parkinson's disease.
