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 Circuits

Neural Circuits

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...

You might also read

Related Articles

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

Sort by

Same author

Adhesive Hydrogels as Fixation and Regeneration Platforms in Cartilage Surgery: Rethinking Scaffold-Tissue Integration from a Clinical Perspective.

International journal of molecular sciences·2026

Same author

Bridging the Digital Divide: The Mediating Role of Social Engagement in Technology Use and Mental Health.

Journal of aging and health·2026

Same author

Minimally invasive prediction of blood lactate during incremental exercise via heart rate, core body temperature, and sweat-derived indices.

Scientific reports·2026

Same author

Comparison of enteric methane emissions between the respiration chamber and the carbon dioxide method in Holstein heifer.

Animal nutrition (Zhongguo xu mu shou yi xue hui)·2026

Same author

Biodegradable Natural Polymer-Based Drug Delivery Systems for Bone Tissue Engineering.

Medicinal research reviews·2026

Same author

Dietary replacement of soybean meal with heat-treated fermented soybean meal affects milk production and nitrogen efficiency in lactating dairy cows.

Animal bioscience·2026

Same journal

Correction: Jiang et al. Methods for Obtaining One Single Larmor Frequency, Either <i>v</i><sub>1</sub> or <i>v</i><sub>2</sub>, in the Coherent Spin Dynamics of Colloidal Quantum Dots. <i>Nanomaterials</i> 2023, <i>13</i>, 2006.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Correction: Ekman et al. Synthesis, Characterization, and Adsorption Properties of Nitrogen-Doped Nanoporous Biochar: Efficient Removal of Reactive Orange 16 Dye and Colorful Effluents. <i>Nanomaterials</i> 2023, <i>13</i>, 2045.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>-Based Materials and Coatings for De-Icing and Defogging of Wind Turbine Blades: Materials Basis, Structural Design, Engineering Integration, and Future Opportunities.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Influence of the Ripeness Stages of the Precursors on the Optical Characteristics of Carbon Dots Obtained from Valencia Orange Peels (<i>Citrus sinensis</i> L. Osbeck) by Hydrothermal Synthesis.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Insights into ALD Growth of Al-Based Dielectric Stack on 4H-SiC.

Nanomaterials (Basel, Switzerland)·2026

Same journal

Metal-<i>N</i>-Heterocyclic Carbene Porous Organic Polymers as Efficient Bifunctional Water-Splitting Electrocatalysts.

Nanomaterials (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 11, 2025

Chronic Implantation of Multiple Flexible Polymer Electrode Arrays

Chronic Implantation of Multiple Flexible Polymer Electrode Arrays

Published on: October 4, 2019

Binary-Weighted Neural Networks Using FeRAM Array for Low-Power AI Computing.

Seung-Myeong Cho¹, Jaesung Lee¹, Hyejin Jo¹

¹School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea.

Nanomaterials (Basel, Switzerland)

|August 13, 2025

Summary

This summary is machine-generated.

This study introduces a binary-weighted neural network (BWNN) using Ferroelectric RAM (FeRAM) for energy-efficient computing. The FeRAM-based computing-in-memory (CIM) architecture significantly reduces power consumption for AI applications.

Keywords:

FeRAM array binary weighted neural networks compute in memory low-power AI computing processing in memory

More Related Videos

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Related Experiment Videos

Last Updated: Sep 11, 2025

Chronic Implantation of Multiple Flexible Polymer Electrode Arrays

Chronic Implantation of Multiple Flexible Polymer Electrode Arrays

Published on: October 4, 2019

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Area of Science:

Computer Engineering
Artificial Intelligence
Materials Science

Background:

Artificial intelligence (AI) is increasingly deployed on edge devices, demanding energy-efficient implementations.
Existing computing-in-memory (CIM) architectures face limitations in power consumption.
Mobile and IoT domains require low-power AI solutions.

Purpose of the Study:

To develop an energy-efficient neural network architecture for edge AI.
To implement a binary-weighted neural network (BWNN) using Ferroelectric RAM (FeRAM)-based synaptic arrays.
To demonstrate the power-saving potential of FeRAM-based CIM.

Main Methods:

Designed a BWNN architecture utilizing FeRAM-based synaptic arrays for CIM.
Leveraged the non-volatile properties and low-power computing of FeRAM.
Simulated power consumption and recognition accuracy using MNIST datasets.

Main Results:

FeRAM-CIM architecture achieved significant reductions in dynamic power (up to 6.5%) and standby power (over 258×).
Binary weight quantization and in-memory computing enabled energy-efficient inference with minimal accuracy loss.
Demonstrated superior energy efficiency (230-580 TOPS/W) compared to SRAM, DRAM, and STT-MRAM CIM.

Conclusions:

FeRAM-based BWNNs offer a compelling solution for energy-constrained edge-AI and IoT applications.
The proposed CIM architecture significantly enhances energy efficiency.
FeRAM technology shows great promise for next-generation low-power AI hardware.