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

The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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.
Neural Circuits01:25

Neural Circuits

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...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Neurons: The Axon01:21

Neurons: The Axon

Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment.
Neurons as Communicators of the Brain01:22

Neurons as Communicators of the Brain

Neurons, the fundamental units of the brain and nervous system, function as the primary transmitters of information throughout the body. Their ability to communicate through electrical and chemical signals is vital for every bodily function, from regulating the heartbeat to processing complex thoughts. Each neuron has three main components: the cell body (soma), dendrites, and an axon, each specialized to facilitate swift and efficient neural communication.
Cell Body
The cell body, also known...
Propagation of Action Potentials01:23

Propagation of Action Potentials

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...

You might also read

Related Articles

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

Sort by
Same author

A simple model of co-emergence of grid and place fields.

ArXiv·2026
Same author

A simple model of co-emergence of grid and place fields.

bioRxiv : the preprint server for biology·2026
Same author

Spatial remapping in the subicular complex and entorhinal cortex follows low-dimensional geometric principles.

bioRxiv : the preprint server for biology·2026
Same author

Long-term evolution of regulatory DNA sequences. Part 1: simulations on global, biophysically-realistic genotype-phenotype maps.

Current opinion in genetics & development·2026
Same author

Long-term evolution of regulatory DNA sequences. Part 2: theory and future challenges.

Current opinion in genetics & development·2026
Same author

A widespread animal communication tempo may resonate with the receiver's brain.

PLoS biology·2026
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

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

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

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

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

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

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

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

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

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

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 2026

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

Optimal population coding by noisy spiking neurons.

Gasper Tkacik1, Jason S Prentice, Vijay Balasubramanian

  • 1Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA. gtkacik@sas.upenn.edu

Proceedings of the National Academy of Sciences of the United States of America
|July 28, 2010
PubMed
Summary
This summary is machine-generated.

Neural networks optimize information coding by adapting pairwise interactions. This dynamic approach balances neural bandwidth efficiency with noise reduction for robust stimulus representation.

More Related Videos

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Related Experiment Videos

Last Updated: Jun 10, 2026

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

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
08:48

Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution

Published on: September 5, 2012

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

  • Computational neuroscience
  • Neural coding
  • Information theory

Background:

  • Collective neural responses in the retina and cortex are often modeled using maximum-entropy principles with pairwise neuronal interactions.
  • Understanding how these interactions are organized to maximize information representation is crucial for deciphering neural computation.

Purpose of the Study:

  • To investigate the optimal organization of pairwise neuronal interactions for maximizing information in population responses.
  • To extend existing models to incorporate stimulus-dependent pairwise interactions.

Main Methods:

  • Extended the linear-nonlinear-Poisson model to include pairwise interactions, creating a stimulus-dependent, pairwise maximum-entropy model.
  • Analyzed how varying single-neuron noise levels and network input distributions affect optimal interaction strategies.

Main Results:

  • Optimal pairwise interactions smoothly transitioned between distinct network functions, including stimulus decorrelation, error correction, and independent encoding.
  • These functions represent a trade-off between efficient use of neural bandwidth and redundancy for noise mitigation.
  • Spontaneous neural activity patterns mirrored stimulus-induced activity, and single-neuron variability overestimated actual network noise.

Conclusions:

  • Brain networks likely adapt their coding principles based on dynamic noise and stimulus correlations, rather than relying on fixed, hardwired architectures.
  • The findings suggest a flexible, adaptive coding strategy in neural systems to optimize information processing under varying conditions.