Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cerebrospinal Fluid

Cerebrospinal Fluid

Cerebrospinal fluid (CSF) is a colorless liquid that flows around the brain and the spinal cord, playing a vital role in the protection, support, and overall function of the central nervous system (CNS). CSF production, circulation, and absorption are tightly regulated processes essential for the brain and spinal cord to function properly.
CSF Production
CSF is produced mainly in the choroid plexus, a network of capillaries and ependymal cells located within the ventricular system of the brain....

You might also read

Related Articles

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

Sort by

Same author

Circulating cell populations as response predictors and targets to improve immunotherapy in metastatic lung cancer.

iScience·2026

Same author

Wavelet-based visual compass.

PloS one·2026

Same author

Introduction to the Proceedings of the CNS*2025 Meeting.

Journal of computational neuroscience·2026

Same author

Efficient event-based delay learning in spiking neural networks.

Nature communications·2025

Same author

Building on models-a perspective for computational neuroscience.

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

Same author

Understanding the mechanism of facilitation in hoverfly TSDNs.

PLoS computational biology·2025

Same journal

A tri-axis optomechanical accelerometer with plasmonic MIM waveguide and structural direction-dependent optical signatures.

Scientific reports·2026

Same journal

Holographic leaky-wave antennas with independently controlled multiple counter-rotating vortex beams.

Scientific reports·2026

Same journal

Differential associations of longitudinal hearing and vision trajectories with dementia and mild cognitive impairment in older adults.

Scientific reports·2026

Same journal

Abdominal obesity and leisure-time sedentary behavior in relation to gastroesophageal reflux disease risk: a prospective cohort study from the UK Biobank.

Scientific reports·2026

Same journal

Effect of nitrogen-rich COF incorporation on the structure and separation performance of polyamide nanofiltration membranes.

Scientific reports·2026

Same journal

Withanolide A inhibits hIAPP aggregation: An In silico, biophysical, and drosophila-based In vivo validation.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 27, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

GeNN: a code generation framework for accelerated brain simulations.

Esin Yavuz¹, James Turner¹, Thomas Nowotny¹

¹Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Brighton, BN1 9QJ, UK.

Scientific Reports

|January 8, 2016

Summary

This summary is machine-generated.

The GeNN (GPU-enhanced Neuronal Networks) framework accelerates brain simulations using graphics processing units (GPUs). This open-source library significantly speeds up computational neuroscience research by optimizing neuronal network models.

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Related Experiment Videos

Last Updated: Mar 27, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Area of Science:

Computational Neuroscience
Neuroscience Simulation
High-Performance Computing

Background:

Large-scale numerical simulations are crucial for understanding brain functions and validating hypotheses.
Computational speed remains a significant bottleneck in simulating realistic, large-scale neuronal network models.

Purpose of the Study:

To introduce the GeNN (GPU-enhanced Neuronal Networks) framework, designed to accelerate neuronal network simulations.
To enable researchers to leverage graphics accelerators for complex brain models without requiring deep technical expertise.

Main Methods:

Development of an open-source library, GeNN, that generates optimized code for NVIDIA GPUs.
Implementation of a flexible and extensible interface for users to define and simulate neuronal networks.
Performance benchmarking of the GeNN framework on large-scale neuronal networks.

Main Results:

Achieved up to a 200-fold speedup for a network of one million conductance-based Hodgkin-Huxley neurons compared to a single CPU core.
Demonstrated varying speedup depending on the specific neuronal model used.
Ensured platform compatibility across Linux, Mac OS X, and Windows.

Conclusions:

The GeNN framework effectively addresses the computational speed challenge in large-scale neuronal network simulations.
GeNN provides a user-friendly and efficient solution for accelerating computational neuroscience research on GPUs.
The framework's open-source nature and comprehensive resources facilitate broader adoption and development.