使用预测错误电路进行不确定性估计
Loreen Hertäg1, Katharina A Wilmes2, Claudia Clopath3
1Modeling of Cognitive Processes, TU Berlin, Berlin, Germany. loreen.hertaeg@tu-berlin.de.
Nature communications
|March 29, 2025
概括
大脑通过使用等级预测错误网络来估计感官和预测不确定性. 这个网络调整了依赖感官输入与基于噪音和环境稳定的预测的依赖,从而影响了感知.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 感知 感知 感知 感知
背景情况:
- 神经回路将感官数据与预测相结合,经常面临相互矛盾的输入.
- 准确的整合需要估计感官刺激和内部预测的不确定性.
- 大脑追踪这些不确定性的机制在很大程度上是未知的.
研究的目的:
- 阐明神经回路如何估计感官和预测不确定性.
- 研究预测错误神经元在不确定性处理中的作用.
- 将不确定性估计与感知偏见联系起来.
主要方法:
- 开发一个层次预测错误网络模型.
- 对噪音感官刺激和预测的神经反应的模拟.
- 在模型中抑制性内部神经元的扰乱,以评估它们的功能.
- 对模型输出的分析,以确定对感知偏差的贡献.
主要成果:
- 一个层次预测错误网络可以成功估计感觉和预测不确定性.
- 积极和消极的预测错误神经元在这个估计中起着不同的作用.
- 该模型表明,电路在杂的感官输入和稳定的环境中更多地依赖预测.
- 抑制性内部神经元干扰揭示了它们在不确定性处理和输入权重中的关键作用.
- 模型模拟将刺激和预测不确定性与感知中观察到的收缩偏差联系起来.
结论:
- 层次预测错误网络为估计神经不确定性提供了一个可行的机制.
- 不确定性估计对于动态加权感官和预测信息至关重要.
- 这些发现提供了对感知偏差的神经基础的见解,特别是收缩偏差.
相关概念视频
03:37Uncertainty in Measurement: Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
01:03Prediction Intervals
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
The...
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:10Propagation of Uncertainty from Systematic Error
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
00:54Uncertainty: Confidence Intervals
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...