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

Scaling01:26

Scaling

609
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
609
Gauss's Law: Problem-Solving01:10

Gauss's Law: Problem-Solving

2.7K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area vector...
2.7K
Uncertainty: Overview00:59

Uncertainty: Overview

1.8K
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.
1.8K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.2K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.2K
Second Law: Motion under Same Force01:10

Second Law: Motion under Same Force

16.4K
Newton's laws can be applied to bodies at rest and bodies in motion. Newton's first law is applied to bodies in equilibrium, whereas the second law applies to accelerating bodies. To study accelerating bodies, first, the directions and magnitudes of acceleration and the applied forces are determined. Then, the free-body diagram is constructed, and Newton's second law is applied, considering the components of the forces in the x and y directions.
Let's imagine a person is...
16.4K
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

You might also read

Related Articles

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

Sort by
Same author

Hippocampal astrocytic sequences emerge during learning and memory.

bioRxiv : the preprint server for biology·2026
Same author

Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning.

Journal of neurophysiology·2025
Same author

Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform.

Proceedings of the National Academy of Sciences of the United States of America·2024
Same author

Ramping cells in rodent mPFC encode time to past and future events via real Laplace transform.

bioRxiv : the preprint server for biology·2024
Same author

Ramping Dynamics in the Frontal Cortex Unfold Over Multiple Timescales During Motor Planning.

bioRxiv : the preprint server for biology·2024
Same author

Internally generated time in the rodent hippocampus is logarithmically compressed.

eLife·2022

Related Experiment Video

Updated: Feb 20, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

614

Neural scaling laws for an uncertain world.

Marc W Howard1, Karthik H Shankar1

  • 1Department of Psychological and Brain Sciences, Boston University.

Psychological Review
|October 17, 2017
PubMed
Summary

Autonomous neural systems require optimal receptor distribution for novel environments. This research proposes a neural uncertainty principle, explaining sensory and cognitive scaling laws like the Weber-Fechner law.

Area of Science:

  • Computational Neuroscience
  • Psychophysics
  • Information Theory

Background:

  • Autonomous neural systems face challenges processing information in diverse environments with varying statistical properties.
  • Efficient information processing is crucial for neural systems operating in novel or unpredictable conditions.

Purpose of the Study:

  • To determine the optimal distribution of receptors along a 1-dimensional continuum for neural systems.
  • To establish design principles for neural representations that minimize assumptions about environmental statistics and maximize information equivalence across different environments.

Main Methods:

  • Derivation based on two core principles: a neural uncertainty principle and maximizing information equivalence.
  • Mathematical modeling of receptor distribution along a 1-dimensional continuum.

Related Experiment Videos

Last Updated: Feb 20, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

614

Main Results:

  • The derived receptor distribution resembles the structure of the human visual system.
  • Provides a theoretical explanation for the Weber-Fechner law in perception.
  • Suggests generalizable scaling relationships beyond sensory input.

Conclusions:

  • The proposed principles offer a general framework for understanding neural representation optimization.
  • Findings extend to neural representations of cognitive quantities like time and numerosity.
  • Highlights the universality of scaling relationships in neural information processing.