Jove
Visualize
Contact Us

Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

314
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
314
Synaptic Signaling01:09

Synaptic Signaling

6.5K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
6.5K
Synaptic Signaling01:12

Synaptic Signaling

79.1K
Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
79.1K
Frequency-dependent Selection01:21

Frequency-dependent Selection

23.0K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
23.0K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

275
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
275
Neural Circuits01:25

Neural Circuits

2.6K
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...
2.6K

You might also read

Related Articles

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

Sort by
Same author

Toward a unified theory of prediction and action.

Neuroscience research·2026
Same author

Triple equivalence in modelling insight and creativity: Classical and quantum perspectives.

Neuroscience research·2026
Same author

WormTracer: A precise method for worm posture analysis using temporal continuity.

Journal of neuroscience methods·2025
Same author

Active Inference and Intentional Behavior.

Neural computation·2025
Same author

On Predictive Planning and Counterfactual Learning in Active Inference.

Entropy (Basel, Switzerland)·2024
Same author

Federated inference and belief sharing.

Neuroscience and biobehavioral reviews·2023
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles
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 Video

Updated: Jan 10, 2026

Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods
07:13

Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods

Published on: March 1, 2024

1.0K

Synaptic pruning facilitates online Bayesian model selection.

Ukyo T Tazawa1, Takuya Isomura2

  • 1Brain Intelligence Theory Unit, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351- 0198, Japan; Department of Systems Science, Graduate School of Informatics, Kyoto University, Kyoto-shi, Kyoto, 606-8501, Japan; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Synaptic pruning, a process where connections are removed, helps brains and AI efficiently learn environmental structures. This Bayesian synaptic model pruning (BSyMP) method optimizes models by removing uninformative connections.

Keywords:
Bayesian model reductionBayesian model selectionFree-energy principleStructure learningSynaptic pruning

More Related Videos

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods
07:13

Author Spotlight: Exploring Glial Influence in Experience-Dependent Synaptic Pruning During Critical Periods

Published on: March 1, 2024

1.0K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K
Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

1.3K

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Artificial intelligence

Background:

  • Identifying optimal structures for generative or world models is crucial for biological and artificial systems.
  • Statistical structure learning is fundamental for efficient information processing.

Purpose of the Study:

  • To introduce a novel synaptic pruning scheme, Bayesian synaptic model pruning (BSyMP), for efficient statistical structure learning.
  • To demonstrate BSyMP's effectiveness in identifying plausible environmental model structures, especially with sparse data.

Main Methods:

  • Extended canonical neural networks to develop a synaptic pruning scheme.
  • Formally equated the scheme to online Bayesian model selection.
  • Utilized connectivity parameters to enable/disable synaptic connections (ON/OFF states).

Main Results:

  • BSyMP parameters converge to zero for uninformative connections, enabling efficient model reduction.
  • BSyMP identified plausible environmental model structures in environments with sparse likelihood and transition matrices.
  • BSyMP demonstrated more efficient model reduction compared to conventional Bayesian model reduction schemes in simulations.

Conclusions:

  • Synaptic pruning is a viable neuronal substrate for structure learning and generalizability in the brain.
  • BSyMP offers a reliable and efficient method for model reduction and structure identification in artificial systems.
  • The findings bridge understanding between biological learning mechanisms and artificial model optimization.