Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Probability Laws01:49

Probability Laws

40.4K
Overview
40.4K
Probability Histograms01:17

Probability Histograms

11.1K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.1K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

100
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
100
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.8K
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Dithering suppresses half-harmonic neural synchronisation to photic stimulation in humans.

Brain stimulation·2026
Same author

Predictive Coding Model Detects Novelty on Different Levels of Representation Hierarchy.

Neural computation·2025
Same author

Dopamine encodes deep network teaching signals for individual learning trajectories.

Cell·2025
Same author

Response of Neuronal Populations to Phase-Locked Stimulation: Model-Based Predictions and Validation.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2025
Same author

Reward Bases: A simple mechanism for adaptive acquisition of multiple reward types.

PLoS computational biology·2024
Same author

Temporal regularities shape perceptual decisions and striatal dopamine signals.

Nature communications·2024

相关实验视频

Updated: Jun 9, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

通过蒙特卡洛预测编码学习感官输入的概率分布.

Gaspard Oliviers1, Rafal Bogacz1, Alexander Meulemans2

  • 1MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.

PLoS computational biology
|October 30, 2024
PubMed
概括

这项研究介绍了蒙特卡洛预测编码 (MCPC),一种新的神经网络模型. MCPC将预测编码与神经采样集成,以学习生成模型并解释感知中的神经变异性.

科学领域:

  • 计算神经科学是一种计算神经科学.
  • 认知科学是一种认知科学.
  • 机器学习是机器学习.

背景情况:

  • 假设大脑使用概率生成模型进行感官解释.
  • 预测编码和神经采样等不同的框架解释了这个过程的不同方面.
  • 变异过以前集成这些框架,引入神经采样到预测编码.

研究的目的:

  • 引入蒙特卡洛预测编码 (MCPC),这是静态输入的变化过的一个变体.
  • 展示MCPC如何整合预测编码和神经采样来学习生成模型.
  • 展示MCPC推断后部分布和产生感官输入的能力.

主要方法:

  • 开发了一个新的神经网络模型:蒙特卡洛预测编码 (MCPC).
  • 使用适应静态输入的变量过原理.
  • 集成的预测编码与神经采样机制.

主要成果:

  • 通过本地计算和可塑性,MCPC学习精确的生成模型.
  • 在MCPC中神经动态推断潜态的后部分布.
  • 在没有实际输入的情况下,MCPC可以产生可能的感官输入.
  • 该模型捕捉了在感知任务期间神经活动变化的实验观测.

更多相关视频

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

相关实验视频

Last Updated: Jun 9, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.8K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

结论:

  • MCPC成功地将预测编码和神经采样结合到一个统一的框架中.
  • 该模型解释了以前由个别框架解释的神经数据.
  • MCPC为最佳感官解释和大脑中神经可变性提供了一个潜在的解释.