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

Long-term Potentiation

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

You might also read

Related Articles

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

Sort by

Same author

Artificial neural network-augmented dosiomic integration for predicting distant recurrence in NSCLC patients treated with SBRT.

Frontiers in oncology·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 11, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Prediction of puncturing events through LSTM for multilayer tissue.

Bulbul Behera¹, M Felix Orlando¹, R S Anand¹

¹Indian Institute of Technology Roorkee, Department of Electrical Engineering, Advanced Robotics Laboratory, Roorkee, Uttarakhand, 247667, India.

Biomedical Physics & Engineering Express

|October 8, 2024

Summary

This study introduces a novel method using Long Short-Term Memory (LSTM) networks to detect penetration events in multilayer tissue. The approach accurately identifies tissue layer transitions using sensor data, enhancing medical procedure safety.

Keywords:

LSTM needle penetration prediction

More Related Videos

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Jun 11, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Predicting In Vivo Payloads Delivery using a Blood-brain Tumor-barrier in a Dish

Published on: April 16, 2019

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Biomedical Engineering
Machine Learning
Robotics

Background:

Accurate detection of penetration events in multilayer tissue is crucial for surgical procedures and medical diagnostics.
Traditional methods may lack the precision and real-time feedback required for complex tissue interactions.

Purpose of the Study:

To develop and validate a novel method for detecting penetration events in multilayer tissue using Long Short-Term Memory (LSTM) networks.
To enhance the precision and safety of medical procedures involving multilayer tissue interaction.

Main Methods:

Collected time-series insertion force data using sensors integrated with a 1-DOF prismatic robot during tissue penetration.
Processed the sensor data with a trained LSTM network to recognize patterns indicative of penetration events across tissue layers.

Main Results:

The LSTM network demonstrated high accuracy and reliability in detecting penetration events in multilayer tissue.
Experimental validation confirmed the effectiveness of the proposed method.

Conclusions:

The developed LSTM-based technique offers a significant advancement over traditional methods for penetration event detection.
This non-invasive, real-time solution improves the precision and safety of medical procedures involving multilayer tissue interaction.