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 Action Potentials01:23

Propagation of Action Potentials

7.2K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
7.2K
Neural Circuits01:25

Neural Circuits

1.7K
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...
1.7K
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

3.3K
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....
3.3K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

398
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
398
Sampling Methods: Overview01:06

Sampling Methods: Overview

569
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
569

You might also read

Related Articles

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

Sort by
Same author

'Backpropagation and the brain' realized in cortical error neuron microcircuits.

PLoS computational biology·2026
Same author

Backpropagation through space, time and the brain.

Nature communications·2025
Same author

Ultrafast neural sampling with spiking nanolasers.

Nature communications·2025
Same author

Building on models-a perspective for computational neuroscience.

Cerebral cortex (New York, N.Y. : 1991)·2025
Same author

Modeling neuron-astrocyte interactions in neural networks using distributed simulation.

PLoS computational biology·2025
Same author

DelGrad: exact event-based gradients for training delays and weights on spiking neuromorphic hardware.

Nature communications·2025

Related Experiment Video

Updated: Sep 29, 2025

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

11.9K

Cortical oscillations support sampling-based computations in spiking neural networks.

Agnes Korcsak-Gorzo1,2,3, Michael G Müller4, Andreas Baumbach1,5

  • 1Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany.

Plos Computational Biology
|March 24, 2022
PubMed
Summary
This summary is machine-generated.

Cortical oscillations help the brain navigate uncertainty by modulating exploration, akin to simulated tempering. This mechanism aids in switching between different interpretations or solutions to problems, enhancing cognitive flexibility.

More Related Videos

Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.3K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

Related Experiment Videos

Last Updated: Sep 29, 2025

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice
07:33

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

11.9K
Generation of Local CA1 γ Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.3K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

Area of Science:

  • Computational Neuroscience
  • Cognitive Science
  • Neurobiology

Background:

  • Brains must represent and manage uncertainty to respond effectively in dynamic environments.
  • Switching between multiple valid interpretations or solutions (the 'mixing problem') is challenging due to dissimilar attractor states.

Purpose of the Study:

  • To propose and investigate the role of cortical oscillations in overcoming the brain's 'mixing problem'.
  • To explore how cortical oscillations can facilitate exploration and switching between cognitive states.

Main Methods:

  • Developed a mathematical framework linking cortical oscillations to simulated tempering.
  • Conducted computer simulations to study the phenomenological implications of this model.

Main Results:

  • Cortical oscillations, through background spiking activity, act as an 'effective temperature' to modulate exploration.
  • Rhythmic changes in neural activity simulate tempering, aiding in state transitions.
  • The model connects cortical oscillations to probabilistic inference, memory replay, and multisensory integration.

Conclusions:

  • Cortical oscillations play a novel computational role in managing uncertainty and cognitive flexibility.
  • This mechanism provides a unified explanation for diverse brain phenomena, including place cell flickering and cue combination.