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 Regulation01:37

Neural Regulation

44.9K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
44.9K
Neural Circuits01:25

Neural Circuits

3.3K
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...
3.3K

You might also read

Related Articles

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

Sort by
Same author

MolluscaGenes: A Transcriptomic Database for the Mollusca.

bioRxiv : the preprint server for biology·2026
Same author

Membrane potential dynamics of peripheral cold-sensitive neurons in <i>Drosophila</i> larvae.

bioRxiv : the preprint server for biology·2026
Same author

The wiring diagram of an entire animal.

eLife·2025
Same author

Social Predation by a Nudibranch Mollusc.

Integrative organismal biology (Oxford, England)·2025
Same author

Synaptic delays shape dynamics and function in multimodal neural motifs.

Chaos (Woodbury, N.Y.)·2025
Same author

Widespread neuronal chaos induced by slow oscillating currents.

Chaos (Woodbury, N.Y.)·2025
Same journal

Anterior Cingulate Cortex Mediates State-Dependent Prioritization of Distressed Conspecifics.

Brain sciences·2026
Same journal

Hemispherotomy for Pediatric Post-Traumatic Epilepsy.

Brain sciences·2026
Same journal

When Robots Learn: Artificial Intelligence and the Next Human-Centered Era of Neurorehabilitation.

Brain sciences·2026
Same journal

The Association Between Changes in White Matter Microstructure and Cognitive Function in Older Adults with Mild Cognitive Impairment.

Brain sciences·2026
Same journal

Beyond Ventricular Enlargement: Multimodal MRI Assessment Improves Surgical Decision-Making in Normal Pressure Hydrocephalus.

Brain sciences·2026
Same journal

The Effects of Personalized Observation, Execution, and Mental Imagery (POEM) Therapy in Logopenic Primary Progressive Aphasia: A Telepractice-Based Single-Case Study.

Brain sciences·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks.

Jassem Bourahmah1, Akira Sakurai1, Paul S Katz2

  • 1Neuroscience Institute, Georgia State University, 100 Piedmont Ave., Atlanta, GA 30303, USA.

Brain Sciences
|May 25, 2024
PubMed
Summary
This summary is machine-generated.

We developed a new objective method to compare biological and mathematical neural models, reducing manual effort. This approach uses biological data to train models, enabling efficient and accurate analysis of neural circuitry.

Keywords:
CPGblended approachmodel trainingneural networkparameter optimization

More Related Videos

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

10.3K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.8K

Related Experiment Videos

Last Updated: Apr 6, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
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

10.3K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

1.8K

Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Biophysics

Background:

  • Comparing biological neural recordings with mathematical models is crucial but often subjective and time-consuming.
  • Existing methods lack objectivity and efficiency for high-throughput analysis.
  • Developing quantitative measures for model comparison is essential for advancing neuroscience.

Purpose of the Study:

  • To introduce a novel, objective, and efficient "blended" system approach for comparing biological and mathematical neural models.
  • To develop quantitative measures (error function) that reduce subjectivity in model comparison.
  • To facilitate parameter optimization for mathematical models using biological data.

Main Methods:

  • Utilized voltage recordings from biological neurons to drive and train mathematical models.
  • Incorporated measurements like action potential frequency, voltage averages/envelopes, and post-synaptic channel probability for calibration.
  • Employed a grid search of synapse conductance during comparison of biological and simulated neurons using the *Melibe leonina* swim central pattern generator (CPG).

Main Results:

  • The "blended" system approach provides an objective, high-throughput, and computationally efficient method for model comparison.
  • A weighted sum of simple functions was found to be crucial for accurately capturing neuronal rhythmic activity.
  • The method successfully facilitated parameter optimization through derived error functions.

Conclusions:

  • The proposed blended system approach offers a significant advancement for comparing biological and mathematical models in neuroscience.
  • This method holds promise for accelerating research in neural circuitry by enabling objective and efficient model validation.
  • The quantitative measures developed can enhance the understanding and development of neural models.