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 Experiment Videos

Energy-efficient coding with discrete stochastic events.

Susanne Schreiber1, Christian K Machens, Andreas V M Herz

  • 1Institute of Biology, Humboldt-University Berlin, 10115 Berlin, Germany. susanne@salk.edu

Neural Computation
|May 22, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

A multi-ring shifter network computes head direction in zebrafish.

Current biology : CB·2026
Same author

Flexible route planning and rapid structure learning by mice in complex environments.

bioRxiv : the preprint server for biology·2026
Same author

Viable fast distinction of human temporal lobe cortical layer 2/3 neurons based on electrical properties.

Journal of neuroscience methods·2026
Same author

Input-dependent directionality of interactions between cortical areas.

bioRxiv : the preprint server for biology·2026
Same author

Low-dimensional population dynamics in the brainstem gate REM sleep.

Nature neuroscience·2026
Same author

Investments in photoreceptors compete with investments in optics to determine eye design.

eLife·2026
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

This study reveals optimal numbers of ion channels for energy-efficient signaling. Energy efficiency in neural communication is maximized by balancing signaling costs, system maintenance, input reliability, and noise.

Area of Science:

  • Biophysics
  • Computational Neuroscience
  • Systems Biology

Background:

  • Biological systems rely on signaling mechanisms, often involving discrete stochastic events like ion channel gating, to transfer information.
  • Understanding the energy efficiency of these signaling processes is crucial for comprehending cellular and neural function, especially under energy constraints.

Purpose of the Study:

  • To investigate the energy efficiency of information transfer via discrete stochastic events, specifically focusing on ion channel mechanisms.
  • To determine the optimal number of ion channels that maximize energy efficiency in generating graded electrical signals.
  • To analyze how various factors, including signaling costs, system maintenance, input reliability, noise, and signal statistics, influence energy efficiency.

Main Methods:

Related Experiment Videos

  • Development of a simplified biophysical model for graded electrical signal generation using sodium and potassium channels.
  • Analysis of energy efficiency as a function of channel number and system parameters.
  • Investigation of the impact of input signal statistics on energy efficiency.

    • Main Results:

    • Identified optimal numbers of ion channels that maximize energy efficiency in signaling.
    • Demonstrated that these optima are influenced by signaling costs, fixed maintenance costs, input reliability, noise levels, and upstream/downstream mechanism costs.
    • Found that energy-efficient signal ensembles, particularly when energy is scarce, favor bimodal distributions of channel activations and minimize large inputs.

    Conclusions:

    • Energy use is a significant constraint that shapes biological signaling mechanisms.
    • Trade-offs between information transfer and energy expenditure strongly influence the number of signaling molecules and synapses in neurons.
    • The manner in which neural mechanisms represent information is critically dependent on energy availability and efficiency considerations.