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

Neural Regulation

Neural Regulation

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.

You might also read

Related Articles

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

Sort by

Same author

Supportive care needs, decision-making participation attitudes, and quality of life in young and middle-aged Chinese patients with cancer: A latent profile and mediation analysis.

Asia-Pacific journal of oncology nursing·2026

Same author

Construction and validation of a novel prognostic risk model in cervical cancer: integrating lactylation-related genes linked to radiotherapy‑associated transcriptional changes.

Discover oncology·2026

Same author

Optimizing core diameter and number for CK20 immunohistochemistry in colorectal cancer tissue microarrays: A virtual TMA simulation study.

Annals of diagnostic pathology·2026

Same author

Circulating Tumor DNA in Tracking Minimal Residual Disease and Recurrence in Ovarian Cancer: A Retrospective Cohort.

Cancer investigation·2026

Same author

Preoperative MMR testing in colorectal cancer: Large-scale cohort confirms reliability but identifies tumor heterogeneity as a challenge.

Pathology, research and practice·2026

Same author

BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.

... International Conference on Learning Representations·2026

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 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

Modulation of Nonlinear Neural Dynamics for Closed-Loop Deep Brain Stimulation Systems.

Yanyu Li, Maryam M Shanechi

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

Summary

This summary is machine-generated.

We developed a novel neural network framework to accurately model complex brain dynamics for advanced closed-loop deep brain stimulation (DBS). This approach outperforms linear models in controlling neurological disorders.

More Related Videos

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

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

Published on: November 12, 2019

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Related Experiment Videos

Last Updated: Jan 9, 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

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

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

Published on: November 12, 2019

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Accurate modeling of neural dynamics is crucial for advancing closed-loop neuromodulation systems.
Current closed-loop deep brain stimulation (DBS) often relies on simple thresholding, limiting its efficacy.
Linear state-space models (LSSMs) struggle to capture the nonlinearities inherent in neural dynamics.

Purpose of the Study:

To design and evaluate advanced modeling and neuromodulation strategies capable of capturing nonlinear neural dynamics.
To compare the performance of a Koopman operator-based approach and a neural network-based approach for closed-loop DBS simulations.
To demonstrate the potential of these methods for future closed-loop neuromodulation system development.

Main Methods:

A Koopman operator-based approach was designed to represent nonlinear systems in a higher-dimensional linear space.
A recurrent neural network (RNN) based framework was developed to capture nonlinear dynamic patterns.
The RNN framework was coupled with the iterative linear quadratic regulator (iLQR) for feedback control.

Main Results:

The Koopman operator approach showed limitations in modeling complex neural dynamics due to observation function selection and data constraints.
The RNN-iLQR framework demonstrated superior performance in system identification and control accuracy compared to LSSMs and the Koopman operator.
Biologically inspired closed-loop DBS simulations validated the effectiveness of the RNN-iLQR approach.

Conclusions:

The RNN-iLQR framework offers a promising solution for modeling complex nonlinear neural dynamics in closed-loop neuromodulation.
This approach has the potential to significantly improve the efficacy and precision of DBS therapies.
Further development of this method could lead to more sophisticated and personalized treatments for neurological and neuropsychiatric disorders.