Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.0K
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...
2.0K
Survival Tree01:19

Survival Tree

449
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
449
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.5K
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...
1.5K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

370
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
370
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

540
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
540
Neural Regulation01:37

Neural Regulation

43.7K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
43.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Decision reversals in sequential decision-making.

Communications psychology·2026
Same author

Reply to 'Boundary issues for multidimensional frameworks of representation'.

Nature reviews. Neuroscience·2026
Same author

Distilling noise characteristics and prior expectations in multisensory causal inference.

PLoS computational biology·2026
Same author

Clarifying the conceptual dimensions of representation in neuroscience.

Nature reviews. Neuroscience·2026
Same author

A megastudy of behavioral interventions to catalyze public, political, and financial climate advocacy.

PNAS nexus·2026
Same author

Dynamics of working memory drift and information flow across the cortical hierarchy.

Proceedings of the National Academy of Sciences of the United States of America·2026

Related Experiment Videos

Efficient probabilistic inference in generic neural networks trained with non-probabilistic feedback.

A Emin Orhan1, Wei Ji Ma2,3

  • 1Center for Neural Science, New York University, New York, NY, 10003, USA. aeminorhan@gmail.com.

Nature Communications
|July 27, 2017
PubMed
Summary

Generic neural networks trained with error-based learning achieve near-optimal probabilistic inference across various tasks. This approach efficiently models uncertainty representation and decision-making without task-specific programming.

Related Experiment Videos

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Cognitive Science

Background:

  • Animals exhibit near-optimal probabilistic inference in psychophysical tasks.
  • Probabilistic inference necessitates representing and utilizing trial-to-trial uncertainties.
  • Prior methods relied on task-specific neural network operations.

Purpose of the Study:

  • To demonstrate that generic neural networks can perform near-optimal probabilistic inference.
  • To show that a simple error-based learning rule is sufficient for this capability.
  • To investigate the efficiency and emergent properties of such networks.

Main Methods:

  • Training generic neural networks with a simple error-based learning rule.
  • Evaluating performance across nine common psychophysical tasks.
  • Analyzing network behavior, neuron requirements, and coding strategies.

Main Results:

  • Generic networks achieved near-optimal probabilistic inference in all tested tasks.
  • Error-based learning explained a monkey's learning curve and choice behavior evolution.
  • Networks demonstrated sublinear growth in neuron count with input size.
  • A novel sparsity-based probabilistic population code emerged.

Conclusions:

  • Probabilistic inference naturally emerges in generic neural networks via error-based learning.
  • This approach offers a more efficient and generalizable method for modeling cognitive computations.
  • The findings challenge the necessity of hand-crafted operations for complex inference tasks.