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

Neural Circuits01:25

Neural Circuits

2.5K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.5K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

275
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
275
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.5K
A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
3.5K

You might also read

Related Articles

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

Sort by
Same author

Personalized whole-brain Ising models with heterogeneous nodes capture differences among brain regions.

bioRxiv : the preprint server for biology·2026
Same author

Simultaneously determining regional heterogeneity and connection directionality from neural activity and symmetric connection.

PLoS computational biology·2025
Same author

Human cortex organizes dynamic co-fluctuations along sensation-association axis.

bioRxiv : the preprint server for biology·2025
Same author

Enhancing visual perception by modulating prestimulus alpha and beta power with tRNS.

Communications biology·2025
Same author

Transfer Learning for Predicting ncRNA-Protein Interactions.

Journal of chemical information and modeling·2025
Same author

The effects of the post-delay epochs on working memory error reduction.

PLoS computational biology·2025

Related Experiment Video

Updated: Dec 23, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

476

Understanding the computation of time using neural network models.

Zedong Bi1,2,3,4,5, Changsong Zhou6,3,4,5,7,8

  • 1Institute for Future, Qingdao University, Shandong 266071, China.

Proceedings of the National Academy of Sciences of the United States of America
|April 29, 2020
PubMed
Summary

Neural networks learn to perceive and process time by tracking state changes, similar to how animals use temporal information for decision-making and anticipating events.

Keywords:
interval timingneural network modelpopulation coding

Related Experiment Videos

Last Updated: Dec 23, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

476

Area of Science:

  • Computational Neuroscience
  • Neural Networks
  • Animal Behavior

Background:

  • Animals need to understand time to maximize rewards in dynamic environments.
  • Key questions remain about how animals process temporal information in working memory and alongside other cognitive functions.
  • The neural mechanisms underlying temporal processing are not fully understood.

Purpose of the Study:

  • To investigate the computational principles of temporal processing in neural networks.
  • To understand how neural networks perceive, maintain, and use time intervals.
  • To explore the interplay between temporal, spatial, and decision-making information.

Main Methods:

  • Supervised training of recurrent neural network (RNN) models.
  • Analysis of state evolution and neuronal firing rates within the RNNs.
  • Investigating the coding geometry of temporal and non-temporal information.

Main Results:

  • Neural networks perceive time via state trajectory evolution.
  • Time intervals are maintained in working memory through firing rate changes.
  • Networks compare/produce time intervals by adjusting state evolution speed.
  • Temporal and non-temporal information are encoded in orthogonal subspaces.
  • Identified four factors contributing to temporal signals in non-timing tasks.

Conclusions:

  • Neural networks provide a framework for understanding fundamental computational principles of temporal processing.
  • The findings offer insights into how animals might process time and predict future events.
  • The study bridges computational modeling with experimental neuroscience, offering testable predictions.